MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices | Paper Citation Record

Observation 972d4f17-16b2-48b7-bedc-ae6d1a880f3b · outbound

This paper cites Bias loss for mobile neural networks.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Bias loss for mobile neural networks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:03.476649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e128f4dd-6588-481a-ab37-6fbd1a3da6fb · outbound

This paper cites Uw- mamba: Underwater image enhancement with state space model.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Uw- mamba: Underwater image enhancement with state space model

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:03.568616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7701cdb4-986b-4ebf-97fa-c8ec3fb5227a · outbound

This paper cites Beyond self-attention: Deformable large kernel attention for medi- cal image segmentation.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Beyond self-attention: Deformable large kernel attention for medi- cal image segmentation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:03.706741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 898d0ac8-54bc-42c5-b801-05d70c4e496a · outbound

This paper cites Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:03.902201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d3c5b60-fb3d-4208-82eb-22290966a8be · outbound

This paper cites A general and adaptive robust loss func- tion.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices A general and adaptive robust loss func- tion

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:04.066098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5d94412-de4f-4a59-b482-0e21785ab53e · outbound

This paper cites Retinexformer: One-stage retinex-based transformer for low-light image enhance- ment.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Retinexformer: One-stage retinex-based transformer for low-light image enhance- ment

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:04.233922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04579d9e-a9d8-42af-b1ab-9c5cfada9ec6 · outbound

This paper cites RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:47:12.006836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 058051d9-cd1d-43a3-b63a-27de0ab9db47 · outbound

This paper cites Vanillanet: the power of minimalism in deep learning.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Vanillanet: the power of minimalism in deep learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:04.559986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20e711d8-efd2-4b93-8ad2-c31beeea823b · outbound

This paper cites Run, don’t walk: chasing higher flops for faster neural networks.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Run, don’t walk: chasing higher flops for faster neural networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:04.727500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8167328-9a46-443c-b0ef-159f02c3d800 · outbound

This paper cites Simple baselines for image restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Simple baselines for image restoration

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:04.859799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d00a7ffc-2640-4d58-b915-73162645aeaa · outbound

This paper cites Mofa: A model simplification roadmap for image restoration on mobile devices.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Mofa: A model simplification roadmap for image restoration on mobile devices

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:05.023513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28085571-ffa0-44e9-bc3c-891fe28310b1 · outbound

This paper cites Gcam: lightweight image inpainting via group convolution and attention mechanism.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Gcam: lightweight image inpainting via group convolution and attention mechanism

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:05.201135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81fe5f65-ab61-4c96-90c0-50398e2c511e · outbound

This paper cites MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPs.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPs

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:47:11.981063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a0f308cf-11ad-4bc2-809c-b43e4753c36d · outbound

This paper cites Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:05.534456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eafc4ef1-20de-4a81-936c-840182a3caff · outbound

This paper cites Reciprocal attention mixing transformer for lightweight image restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Reciprocal attention mixing transformer for lightweight image restoration

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:05.694644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 530dd81b-4b86-444d-a543-62ec13523a52 · outbound

This paper cites Efficient deep models for real-time 4k image super-resolution.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Efficient deep models for real-time 4k image super-resolution

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:05.869300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e941b11-78c0-4059-b018-77547b50687c · outbound

This paper cites Focal network for image restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Focal network for image restoration

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:06.024521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c56be552-bc9b-477b-a9b5-630887c30533 · outbound

This paper cites Image restoration via frequency selection.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Image restoration via frequency selection

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:06.134545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1a42810-cc39-46b6-8d4a-8625ed2ad2ff · outbound

This paper cites Revitalizing convolutional network for image restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Revitalizing convolutional network for image restoration

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:06.240472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 253ea82b-b599-49b1-b049-79639dab6ddd · outbound

This paper cites You only need 90k parameters to adapt light: a light weight trans- former for image enhancement and exposure correction.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices You only need 90k parameters to adapt light: a light weight trans- former for image enhancement and exposure correction

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:06.343698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ff0e1a6-67c6-480a-b8ab-850a4c502c76 · outbound

This paper cites Awnet: Attentive wavelet network for image isp.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Awnet: Attentive wavelet network for image isp

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:06.445265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6409163-edce-4357-b280-6e437614fb1a · outbound

This paper cites Acnet: Strengthening the kernel skeletons for power- ful cnn via asymmetric convolution blocks.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Acnet: Strengthening the kernel skeletons for power- ful cnn via asymmetric convolution blocks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:06.548522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f91f38d4-ac18-4adf-a323-8f2d341030e1 · outbound

This paper cites Diverse branch block: Building a con- volution as an inception-like unit.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Diverse branch block: Building a con- volution as an inception-like unit

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:06.628487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14a1a361-94f6-496a-9f8f-43f0ec5e51c2 · outbound

This paper cites Repvgg: Making vgg- style convnets great again.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Repvgg: Making vgg- style convnets great again

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:06.719654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 901aa690-feac-48b7-bb09-d7429efc43ef · outbound

This paper cites Un- derwater depth estimation and image restoration based on single images.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Un- derwater depth estimation and image restoration based on single images

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.262211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b4c644aa-1b46-4c98-b490-aca6c1be77ea · outbound

This paper cites Uncertainty inspired underwater image en- hancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Uncertainty inspired underwater image en- hancement

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.246492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 92d902e9-621a-475b-beff-e6edb71322b7 · outbound

This paper cites Learning a simple low-light im- age enhancer from paired low-light instances.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learning a simple low-light im- age enhancer from paired low-light instances

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.229544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 48553bb0-ca58-4dae-bf8f-e4ca9ee85609 · outbound

This paper cites Syenet: A simple yet effective net- work for multiple low-level vision tasks with real-time per- formance on mobile device.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Syenet: A simple yet effective net- work for multiple low-level vision tasks with real-time per- formance on mobile device

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.212959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0325b59c-634a-45b1-a7d7-2b86a6ebe0b8 · outbound

This paper cites Zero- reference deep curve estimation for low-light image en- hancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Zero- reference deep curve estimation for low-light image en- hancement

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.192867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5bb4c8cc-e543-4385-898c-84d512dff991 · outbound

This paper cites Ghostnet: More features from cheap operations.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Ghostnet: More features from cheap operations

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.175812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5c87dedf-1821-4745-911a-63463a679d30 · outbound

This paper cites Masked autoencoders are scal- able vision learners.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Masked autoencoders are scal- able vision learners

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.159097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bc526e2f-f7f2-4dae-8b95-9e1f59df9e9c · outbound

This paper cites Enhancing raw-to-srgb with decoupled style structure in fourier domain.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Enhancing raw-to-srgb with decoupled style structure in fourier domain

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.142916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b2ef20fd-9506-4647-8fbb-4ae00afcfec4 · outbound

This paper cites Searching for mo- bilenetv3.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Searching for mo- bilenetv3

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.126116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1f05b55e-1de5-4a57-8140-41f1ef853ae8 · outbound

This paper cites Squeeze-and-excitation networks.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Squeeze-and-excitation networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.110002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f85d6161-6301-4794-b82e-0f06cdc3e46a · outbound

This paper cites Aim 2020 challenge on learned image signal processing pipeline.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Aim 2020 challenge on learned image signal processing pipeline

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.094544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 506d990b-ddac-41e1-939b-832abb4af8d3 · outbound

This paper cites Replac- ing mobile camera isp with a single deep learning model.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Replac- ing mobile camera isp with a single deep learning model

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.079260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 37a9a012-ba65-4973-9ff0-542c6945cbf1 · outbound

This paper cites Learned smartphone isp on mobile npus with deep learning, mobile ai 2021 chal- lenge: Report.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learned smartphone isp on mobile npus with deep learning, mobile ai 2021 chal- lenge: Report

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.064280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 80048a37-0262-47dc-9edd-c54c76f63447 · outbound

This paper cites Learned smartphone isp on mobile gpus with deep learning, mobile ai & aim 2022 challenge: report.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learned smartphone isp on mobile gpus with deep learning, mobile ai & aim 2022 challenge: report

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.048554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e37f5857-05ae-4ff2-8123-73ba6912fccd · outbound

This paper cites Fast un- derwater image enhancement for improved visual percep- tion.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Fast un- derwater image enhancement for improved visual percep- tion

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.033256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 72db4925-0f40-4235-9e0f-1d4ac85ede35 · outbound

This paper cites Low-light image enhancement with wavelet-based diffusion models.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Low-light image enhancement with wavelet-based diffusion models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:13.015837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d763b9e9-5352-41b2-8ee1-2718a1b18053 · outbound

This paper cites Five a+ net- work: You only need 9k parameters for underwater image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Five a+ net- work: You only need 9k parameters for underwater image enhancement

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.995669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d7756994-2c5b-45ab-aa3a-327d1afe92cb · outbound

This paper cites Spectroformer: Multi-domain query cascaded transformer network for underwater image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Spectroformer: Multi-domain query cascaded transformer network for underwater image enhancement

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.975476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 200b69f8-f25d-46f7-8058-35e202b8006c · outbound

This paper cites Feature modulation transformer: Cross-refinement of global representation via high-frequency prior for image super-resolution.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Feature modulation transformer: Cross-refinement of global representation via high-frequency prior for image super-resolution

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.957017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 36339044-f2d4-4112-a761-e752602cf489 · outbound

This paper cites An underwater image enhancement benchmark dataset and beyond.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices An underwater image enhancement benchmark dataset and beyond

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.940598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 41b48644-296a-40d2-adfe-b839a1c5b94b · outbound

This paper cites Learning to enhance low-light image via zero-reference deep curve estimation.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learning to enhance low-light image via zero-reference deep curve estimation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.921812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fb74354a-9a46-4868-ab37-9f3e7027a406 · outbound

This paper cites Ntire 2023 challenge on efficient super- resolution: Methods and results.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Ntire 2023 challenge on efficient super- resolution: Methods and results

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.905832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8e51224e-30ee-4064-b11b-6964d47913c8 · outbound

This paper cites Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.888552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 730c672f-36ba-4aac-a817-d02bbf3e6d74 · outbound

This paper cites Boths: Super lightweight network-enabled under- water image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Boths: Super lightweight network-enabled under- water image enhancement

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.867407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2af5d7de-ee41-46d9-97db-93bf42f1945c · outbound

This paper cites NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:09.138983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 876bbd1b-1676-4e4f-9144-61c1c6801f67 · outbound

This paper cites NAM: Normalization-based Attention Module.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices NAM: Normalization-based Attention Module

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:09.238281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ee74622-ae93-4eca-914d-eda9db5e922a · outbound

This paper cites Toward fast, flexible, and robust low-light image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Toward fast, flexible, and robust low-light image enhancement

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.851369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4964214c-f3b7-449f-9732-be9e06a5eded · outbound

This paper cites Rewrite the stars.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Rewrite the stars

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.834577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c0d7d973-58bf-4129-b0c3-66d197cbe491 · outbound

This paper cites A wavelet-based dual-stream network for underwater image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices A wavelet-based dual-stream network for underwater image enhancement

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.818436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 49c11520-daab-40a4-8916-bbe6dee0bc3d · outbound

This paper cites Shallow-uwnet: Compressed model for underwater image enhancement (student abstract).

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Shallow-uwnet: Compressed model for underwater image enhancement (student abstract)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.801082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aaefe1b2-76ba-432a-8a00-bfa61a389030 · outbound

This paper cites Efficient multi-scale attention module with cross-spatial learning.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Efficient multi-scale attention module with cross-spatial learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.785168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1d5a147b-b015-49da-8c5d-19b557240fea · outbound

This paper cites U-shape trans- former for underwater image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices U-shape trans- former for underwater image enhancement

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.769459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7e9a4182-50ed-49c0-ab4c-debfb5c1b90c · outbound

This paper cites Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:47:11.919695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 47d03f55-278d-43b0-96bb-289261a0d249 · outbound

This paper cites Semi- supervised feature distillation and unsupervised domain ad- versarial distillation for underwater image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Semi- supervised feature distillation and unsupervised domain ad- versarial distillation for underwater image enhancement

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.754332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 26f94d76-bbee-415c-9af7-30b47a9bc5a0 · outbound

This paper cites Double domain guided real- time low-light image enhancement for ultra-high-definition transportation surveillance.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Double domain guided real- time low-light image enhancement for ultra-high-definition transportation surveillance

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.739379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d4e0893d-8c49-4cbe-8d0d-32ee3b79954d · outbound

This paper cites Quantized proximal averaging networks for com- pressed image recovery.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Quantized proximal averaging networks for com- pressed image recovery

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.723497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4a62d7b6-d4f6-4ce6-8e27-40b6fcf6f1ea · outbound

This paper cites The ninth ntire 2024 efficient super- resolution challenge report.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices The ninth ntire 2024 efficient super- resolution challenge report

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.708312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4932f085-7259-4051-a1a6-ddbf3afb891c · outbound

This paper cites Wavelength- based attributed deep neural network for underwater image restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Wavelength- based attributed deep neural network for underwater image restoration

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.693032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 04c8e027-2eb8-4511-b2a2-e915b47f2663 · outbound

This paper cites Efficient attention: Attention with lin- ear complexities.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Efficient attention: Attention with lin- ear complexities

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.677409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2699ab60-0d03-4b1f-ab11-f7c9b4e53114 · outbound

This paper cites Memory-oriented structural pruning for efficient im- age restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Memory-oriented structural pruning for efficient im- age restoration

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.660260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7990f307-1a5f-4321-8e59-01cab9b8407a · outbound

This paper cites Ghostnetv2: Enhance cheap operation with long-range attention.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Ghostnetv2: Enhance cheap operation with long-range attention

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.641148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a5092599-deca-43ba-ac2f-37fcd60baeca · outbound

This paper cites Un- derwater image enhancement by transformer-based diffu- sion model with non-uniform sampling for skip strategy.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Un- derwater image enhancement by transformer-based diffu- sion model with non-uniform sampling for skip strategy

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.622922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 05671936-cedd-47fd-b082-5dea3f16d8af · outbound

This paper cites Mobileone: An improved one millisecond mobile backbone.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Mobileone: An improved one millisecond mobile backbone

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.602920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f4a7b53a-fb6e-40bc-9e8e-972bdf414c72 · outbound

This paper cites Swift parameter-free attention network for efficient super- resolution.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Swift parameter-free attention network for efficient super- resolution

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.587579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c1b9dae6-6a95-44e6-accd-7786961307c0 · outbound

This paper cites Repvit: Revisiting mobile cnn from vit perspective.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Repvit: Revisiting mobile cnn from vit perspective

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.571066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e161b269-9d31-4a61-b282-f8252d2a845a · outbound

This paper cites Cor- relation matching transformation transformers for uhd im- age restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Cor- relation matching transformation transformers for uhd im- age restoration

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.551335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3cbc2d1c-01ac-4677-b0e3-13344bec8cbc · outbound

This paper cites Eca-net: Efficient channel attention for deep convolutional neural networks.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Eca-net: Efficient channel attention for deep convolutional neural networks

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.530620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 800abd42-0e73-4e6b-be1e-016597028e4d · outbound

This paper cites Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.513533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e26ed637-578b-454e-b1b4-1a44d93f1806 · outbound

This paper cites Adversar- ially regularized low-light image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Adversar- ially regularized low-light image enhancement

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.493985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a121da83-297e-4798-94d1-375fd73cfb1d · outbound

This paper cites Tied block convolution: Leaner and better cnns with shared thinner filters.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Tied block convolution: Leaner and better cnns with shared thinner filters

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.477602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 97d991aa-154d-409f-bfa0-d55cd059757c · outbound

This paper cites Repsr: Training efficient vgg-style super-resolution networks with structural re-parameterization and batch normalization.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Repsr: Training efficient vgg-style super-resolution networks with structural re-parameterization and batch normalization

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.462590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fb12a3c7-f668-415d-9baa-029e183d00e8 · outbound

This paper cites Deep retinex decomposition for low-light enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Deep retinex decomposition for low-light enhancement

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.447554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8fda7d9c-9410-4bba-9157-10c2fd31ab1c · outbound

This paper cites An illumination-guided dual attention vision transformer for low-light image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices An illumination-guided dual attention vision transformer for low-light image enhancement

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.431203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 227be130-f876-4123-9dfa-6a4a5bd42dac · outbound

This paper cites Cbam: Convolutional block attention module.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Cbam: Convolutional block attention module

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.415463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 38093d8a-9615-43a1-90df-270fdc7b8489 · outbound

This paper cites Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.400451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 67ff739c-51ee-4ce2-b17e-04393d820e22 · outbound

This paper cites Diffir: Efficient diffusion model for image restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Diffir: Efficient diffusion model for image restoration

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.384377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 254499e8-6592-4076-9609-024f95da6f44 · outbound

This paper cites Boosting image restoration via priors from pre-trained models.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Boosting image restoration via priors from pre-trained models

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.363918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e02752bd-d1aa-4d4d-ade1-195c09fbae1c · outbound

This paper cites From fidelity to perceptual quality: A semi- supervised approach for low-light image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices From fidelity to perceptual quality: A semi- supervised approach for low-light image enhancement

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.345467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fefc34f9-54f3-4cfc-9527-8b8bb80df259 · outbound

This paper cites Accelir: Task-aware image compression for accelerating neural restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Accelir: Task-aware image compression for accelerating neural restoration

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.329488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8d98691a-77b9-48f1-a3c3-feb9d1eeef88 · outbound

This paper cites Diffraw: Leveraging diffusion model to generate dslr- comparable perceptual quality srgb from smartphone raw images.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Diffraw: Leveraging diffusion model to generate dslr- comparable perceptual quality srgb from smartphone raw images

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.313073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c43df305-9d51-4e77-b626-01007652102b · outbound

This paper cites Diff-retinex: Rethinking low-light image enhancement with a generative diffusion model.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Diff-retinex: Rethinking low-light image enhancement with a generative diffusion model

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.296442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fda1d60f-02ec-467a-9a8f-98caf64cdf0b · outbound

This paper cites Learning enriched features for real image restora- tion and enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learning enriched features for real image restora- tion and enhancement

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.278811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 54a25b44-d048-41a3-8747-b3ccd91e64e4 · outbound

This paper cites Learning image-adaptive 3d lookup tables for high performance photo enhancement in real-time.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learning image-adaptive 3d lookup tables for high performance photo enhancement in real-time

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.260482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c271737c-a15f-4295-940d-6cde7ffa532b · outbound

This paper cites Rethinking mobile block for efficient attention-based models.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Rethinking mobile block for efficient attention-based models

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.242098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e516f4cc-1be4-4f1e-a7e8-d54f478e6d9a · outbound

This paper cites Repnas: Searching for efficient re-parameterizing blocks.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Repnas: Searching for efficient re-parameterizing blocks

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.223563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6b5ad6b3-8731-400a-a7d4-cf8ea33dbd89 · outbound

This paper cites Liteenhancenet: A lightweight network for real-time single underwater image enhancement.Expert Systems with Applications, 240:122546, 2024.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Liteenhancenet: A lightweight network for real-time single underwater image enhancement.Expert Systems with Applications, 240:122546, 2024

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.208494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f108411c-a280-4562-984e-e567414ff67d · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural net- work for mobile devices.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Shufflenet: An extremely efficient convolutional neural net- work for mobile devices

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.192120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 48dc6547-86ad-4832-8d6e-8ef3d0d0a194 · outbound

This paper cites Edge-oriented convolution block for real-time super resolution on mobile devices.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Edge-oriented convolution block for real-time super resolution on mobile devices

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.174036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8444728b-ed26-4bf5-8d40-225dad4fa741 · outbound

This paper cites LLEMamba: Low-Light Enhancement via Relighting-Guided Mamba with Deep Unfolding Network.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices LLEMamba: Low-Light Enhancement via Relighting-Guided Mamba with Deep Unfolding Network

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:11.785971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93d61435-8de9-4aea-9cd8-d1a63b03a132 · outbound

This paper cites Beyond brightening low-light images.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Beyond brightening low-light images

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.155712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3cd9a218-dd66-4b3c-ba24-55127c268761 · outbound

This paper cites Learning raw-to-srgb mappings with inaccurately aligned supervision.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learning raw-to-srgb mappings with inaccurately aligned supervision

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.137202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cfa5fdde-77c1-417d-bf0b-4cb5b9cd3b3b · outbound

This paper cites Wavelet-based fourier information interaction with fre- quency diffusion adjustment for underwater image restora- tion.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Wavelet-based fourier information interaction with fre- quency diffusion adjustment for underwater image restora- tion

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.119738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a5b761d3-45c8-4185-950a-9fd25768d333 · outbound

This paper cites To- ward sufficient spatial-frequency interaction for gradient- aware underwater image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices To- ward sufficient spatial-frequency interaction for gradient- aware underwater image enhancement

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.101228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 48c14eda-ad24-4c06-afce-8617b9794f21 · outbound

This paper cites Semantic-guided zero-shot learning for low-light image/video enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Semantic-guided zero-shot learning for low-light image/video enhancement

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.081244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 96133384-9eaf-4a48-914d-dcb206cc047c · outbound

This paper cites A 7K Parameter Model for Underwater Image Enhancement based on Transmission Map Prior.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices A 7K Parameter Model for Underwater Image Enhancement based on Transmission Map Prior

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:11.819282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f4f00a9-08eb-44dd-9694-537c1b85e94c · outbound

This paper cites Ac- celerate cnn via recursive bayesian pruning.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Ac- celerate cnn via recursive bayesian pruning

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.064810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers