RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report | Paper Citation Record

Observation 3af92e6a-69ad-4b00-bc32-d07a4600d369 · outbound

This paper cites Semi-supervised raw-to-raw mapping.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Semi-supervised raw-to-raw mapping

Reference 1

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Observation 7eecde94-256e-4027-9d7e-6045eccd4036 · outbound

This paper cites ReRAW: RGB-to-RAW Image Reconstruction via Stratified Sampling for Efficient Object Detection on the Edge.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report ReRAW: RGB-to-RAW Image Reconstruction via Stratified Sampling for Efficient Object Detection on the Edge

Reference 2

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RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Unresolved cited work

Reference 3

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Observation 18e577f9-a747-4219-8085-d20d1ac79f82 · outbound

This paper cites Unprocessing images for learned raw denoising.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Unprocessing images for learned raw denoising

Reference 4

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This paper cites Transunet: Rethinking the u-net architec- ture design for medical image segmentation through the lens of transformers.Medical Image Analysis, 97:103280, 2024.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Transunet: Rethinking the u-net architec- ture design for medical image segmentation through the lens of transformers.Medical Image Analysis, 97:103280, 2024

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:49.876935Z

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Observation fc7f80bc-0bab-44af-a83c-705ba6fa9d23 · outbound

This paper cites Simple Baselines for Image Restoration.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Simple Baselines for Image Restoration

Reference 6

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Observation 9b78b9b9-ddc1-4c7c-a2af-5223eca5c80d · outbound

This paper cites Computational optics for mobile terminals in mass production.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4):4245–4259, 2022.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Computational optics for mobile terminals in mass production.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4):4245–4259, 2022

Reference 7

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Observation bf4004c0-1c9e-4476-b1a8-5e508fd9665f · outbound

This paper cites Dynamic convolu- tion: Attention over convolution kernels.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Dynamic convolu- tion: Attention over convolution kernels

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:49.363478Z

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Observation e5e42a22-5aa3-4a4a-b3b7-b85002af6b6f · outbound

This paper cites NTIRE 2025 challenge on image super-resolution (×4): Methods and results.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on image super-resolution (×4): Methods and results

Reference 9

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Observation c529f93a-9770-4d9d-83f9-88a38491c515 · outbound

This paper cites NTIRE 2025 challenge on real-world face restoration: Methods and results.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on real-world face restoration: Methods and results

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:33:34.812893Z

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Observation c4eb2ff5-9e24-4a6c-8158-d4ff4dc92d01 · outbound

This paper cites NTIRE 2025 challenge on raw image restoration and super-resolution.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on raw image restoration and super-resolution

Reference 11

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Observation 3ebcde0b-c15e-473c-a8f6-fffd62eca5c7 · outbound

This paper cites Raw image reconstruc- tion from RGB on smartphones.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Raw image reconstruc- tion from RGB on smartphones

Reference 12

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Observation 07183b83-a241-4a2b-946c-1de258735001 · outbound

This paper cites Model-based image signal processors via learnable dictionaries.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Model-based image signal processors via learnable dictionaries

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:49.014574Z

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Observation 263c6624-dd18-4379-a68e-1a709b1a1218 · outbound

This paper cites Reversed image sig- nal processing and raw reconstruction.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Reversed image sig- nal processing and raw reconstruction

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:48.692443Z

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Observation 2a1f5c4e-56ae-4151-b750-bde3ba6aeb91 · outbound

This paper cites Bsraw: Improving blind raw image super-resolution.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Bsraw: Improving blind raw image super-resolution

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:48.481389Z

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Observation 2de6744e-8466-4f10-b0fd-85683661e9d5 · outbound

This paper cites To- ward efficient deep blind raw image restoration.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report To- ward efficient deep blind raw image restoration

Reference 16

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Observation b748be9a-9b54-41a7-af1b-1a37b2299ffd · outbound

This paper cites Vision Transformers Need Registers.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Vision Transformers Need Registers

Reference 17

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Observation d2acb515-03e4-4d67-a55a-7315a15afa31 · outbound

This paper cites Mobile Computational Photography: A Tour.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Mobile Computational Photography: A Tour

Reference 18

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Observation 1b99956f-9055-4c81-9654-e823f9dc88c7 · outbound

This paper cites 1m parameters are enough? a lightweight cnn-based model for medical image segmentation.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report 1m parameters are enough? a lightweight cnn-based model for medical image segmentation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:48.005162Z

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Observation 54b5ab47-b3ae-4ef0-81cf-05eeb52ba17e · outbound

This paper cites Rispnet: a network for reversed image signal pro- cessing.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Rispnet: a network for reversed image signal pro- cessing

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:47.671436Z

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Observation bd5ac205-c664-4977-ba65-ba0638b6a3e6 · outbound

This paper cites NTIRE 2025 challenge on night photography rendering.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on night photography rendering

Reference 21

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Observation b639c7c9-d0ff-43cc-ab5b-b6d7451eacb9 · outbound

This paper cites NTIRE 2025 challenge on cross-domain few-shot object detection: Methods and results.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on cross-domain few-shot object detection: Methods and results

Reference 22

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Observation 3dcc98a6-1fc1-4f16-a073-f11a5770f7eb · outbound

This paper cites Deep joint demosaicking and denoising.ACM Transactions on Graphics (ToG), 35(6):1–12, 2016.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Deep joint demosaicking and denoising.ACM Transactions on Graphics (ToG), 35(6):1–12, 2016

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:47.333663Z

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Observation 4854316a-8dd9-4603-a783-bd9644e7cb37 · outbound

This paper cites NTIRE 2025 challenge on text to image generation model quality assess- ment.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on text to image generation model quality assess- ment

Reference 24

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Observation 5924da3e-462f-4e7c-ac62-9053778f8769 · outbound

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

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Replac- ing mobile camera isp with a single deep learning model

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:33:36.675211Z

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Observation 816f3dac-df88-4f5d-8aca-8a9ec198f282 · outbound

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

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Learned smartphone isp on mobile gpus with deep learning, mobile ai & aim 2022 challenge: report

Reference 26

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Observation 6d1e4547-47a8-4b37-a86a-077c2a900a94 · outbound

This paper cites NTIRE 2025 challenge on video quality enhancement for video con- ferencing: Datasets, methods and results.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on video quality enhancement for video con- ferencing: Datasets, methods and results

Reference 27

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Observation 329c17d7-7f92-4058-a2ff-d04de1d749b6 · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Perceptual losses for real-time style transfer and super-resolution

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:46.798948Z

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Observation 8ab86121-ac0d-42c1-bdcc-6c5ce172967e · outbound

This paper cites A software platform for manipulating the camera imaging pipeline.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report A software platform for manipulating the camera imaging pipeline

Reference 29

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This paper cites NTIRE 2025 challenge on efficient burst hdr and restoration: Datasets, methods, and results.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on efficient burst hdr and restoration: Datasets, methods, and results

Reference 30

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This paper cites Omni-Dimensional Dynamic Convolution.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Omni-Dimensional Dynamic Convolution

Reference 31

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This paper cites NTIRE 2025 challenge on day and night raindrop removal for dual-focused images: Methods and results.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on day and night raindrop removal for dual-focused images: Methods and results

Reference 32

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This paper cites NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Kwaisr dataset and study.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Kwaisr dataset and study

Reference 33

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RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Methods and results

Reference 34

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This paper cites NTIRE 2025 the 2nd restore any image model (RAIM) in the wild challenge.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 the 2nd restore any image model (RAIM) in the wild challenge

Reference 35

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Observation f17558a8-f069-4e64-a45e-d4521da6aae2 · outbound

This paper cites Joint demo- saicing and denoising with self guidance.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Joint demo- saicing and denoising with self guidance

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:46.396257Z

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Observation 521c3d25-5c7c-4f96-aac4-26b4c19f8d10 · outbound

This paper cites NTIRE 2025 XGC quality assessment chal- lenge: Methods and results.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 XGC quality assessment chal- lenge: Methods and results

Reference 37

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Observation 5863e077-c59b-4431-9193-85a1fbef8dd9 · outbound

This paper cites NTIRE 2025 challenge on low light image enhancement: Methods and results.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on low light image enhancement: Methods and results

Reference 38

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Observation 649051a9-973c-401c-8206-e82c67980f7f · outbound

This paper cites Image semantic segmentation approach based on deeplabv3 plus network with an attention mech- anism.Engineering Applications of Artificial Intelligence, 127:107260, 2024.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Image semantic segmentation approach based on deeplabv3 plus network with an attention mech- anism.Engineering Applications of Artificial Intelligence, 127:107260, 2024

Reference 39

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Observation 39cbfc2d-4f1f-4f5e-8000-6c2039eac717 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 40

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This paper cites Decoupled Weight Decay Regularization.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Decoupled Weight Decay Regularization

Reference 41

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RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Unresolved cited work

Reference 42

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Observation d9dcbd1d-04ab-461d-b321-368feadf48fb · outbound

This paper cites Spatially aware metadata for raw reconstruction.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Spatially aware metadata for raw reconstruction

Reference 43

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Observation 33d6dee9-ce19-4f80-bd68-2e2674d6d18f · outbound

This paper cites Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline

Reference 44

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Observation 1aedc2b2-8821-41d7-bcc9-51691b204cae · outbound

This paper cites The tenth NTIRE 2025 efficient super- resolution challenge report.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report The tenth NTIRE 2025 efficient super- resolution challenge report

Reference 45

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Observation e2364f2e-bf18-41f5-b0a4-a3ed28f3de42 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 46

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Observation ec47412b-27c2-4039-8047-5a737e70cd2f · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report U-net: Convolutional networks for biomedical image segmentation

Reference 47

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Observation 0e597731-505b-4060-ad2f-520976950d74 · outbound

This paper cites NTIRE 2025 challenge on UGC video enhancement: Meth- ods and results.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on UGC video enhancement: Meth- ods and results

Reference 48

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Observation cc68f651-0973-4ec9-a325-dfe898baf94c · outbound

This paper cites Deepisp: Toward learning an end-to-end image processing pipeline.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Deepisp: Toward learning an end-to-end image processing pipeline

Reference 49

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RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Unresolved cited work

Reference 50

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Observation 0334797e-fe77-4fa4-8f38-fee51e38562e · outbound

This paper cites Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:44.747656Z

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Observation 4c3520a3-bbc1-43cc-8ba8-e22fca02e946 · outbound

This paper cites Cyclical learning rates for training neural networks.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Cyclical learning rates for training neural networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:44.481611Z

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Observation 304282dd-ed7c-4334-a7ca-c2f57702d176 · outbound

This paper cites NTIRE 2025 challenge on event-based image deblurring: Methods and results.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on event-based image deblurring: Methods and results

Reference 53

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Observation 69270f3b-b360-4f52-943c-92ca3c351def · outbound

This paper cites The tenth ntire 2025 image denoising challenge report.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report The tenth ntire 2025 image denoising challenge report

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T11:33:40.186470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4eb8d137-dc6e-4743-a15f-3e135938349e · outbound

This paper cites NTIRE 2025 image shadow removal challenge report.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 image shadow removal challenge report

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T11:33:40.327316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a8ec086-be60-4439-9369-4b55f56f7cb3 · outbound

This paper cites NTIRE 2025 ambi- ent lighting normalization challenge.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 ambi- ent lighting normalization challenge

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T11:33:40.471873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb594d37-999a-4f63-a095-8f0868f01868 · outbound

This paper cites NTIRE 2025 challenge on light field image super-resolution: Methods and results.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on light field image super-resolution: Methods and results

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T11:33:40.635225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5fe85cc-4f5a-4c6b-a851-3ef759ba61ff · outbound

This paper cites Uformer: A general u-shaped transformer for image restoration.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Uformer: A general u-shaped transformer for image restoration

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:44.149789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 61bf17c1-fd87-4e87-a111-ab15d95b0596 · outbound

This paper cites Invertible im- age signal processing.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Invertible im- age signal processing

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T11:33:40.918700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82082369-d8a5-4fb9-acfe-e2740f6d4543 · outbound

This paper cites Condconv: Conditionally parameterized convolu- tions for efficient inference.Advances in neural information processing systems, 32, 2019.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Condconv: Conditionally parameterized convolu- tions for efficient inference.Advances in neural information processing systems, 32, 2019

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:43.888404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b77c3d00-d868-4235-9fd3-945f29f9d548 · outbound

This paper cites NTIRE 2025 challenge on single image reflection removal in the wild: Datasets, methods and results.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on single image reflection removal in the wild: Datasets, methods and results

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T11:33:41.202857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6006d772-e7ed-4d8f-b064-98527d81a58f · outbound

This paper cites NTIRE 2025 challenge on hr depth from images of specular and transparent surfaces.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report NTIRE 2025 challenge on hr depth from images of specular and transparent surfaces

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T11:33:41.339900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd4f9d49-71f9-4722-90ae-c105f33347ab · outbound

This paper cites Cycleisp: Real image restoration via improved data synthesis, 2020.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Cycleisp: Real image restoration via improved data synthesis, 2020

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:43.645915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c007a8fe-04a0-4d22-92a2-4097dcd98a7d · outbound

This paper cites Transcending the limit of local window: Advanced super-resolution transformer with adaptive token dictionary.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Transcending the limit of local window: Advanced super-resolution transformer with adaptive token dictionary

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:43.415119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3f4047a8-5e03-417f-849f-3609cd421b6f · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report The unreasonable effectiveness of deep features as a perceptual metric

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T11:33:41.774181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d5166af-e554-4bfb-825f-f35d7fafa05e · outbound

This paper cites KBNet: Kernel Basis Network for Image Restoration.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report KBNet: Kernel Basis Network for Image Restoration

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T11:33:41.876908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdb1d673-6bee-4681-bf12-36f9d079fd56 · outbound

This paper cites Eednet: enhanced encoder-decoder network for autoisp.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Eednet: enhanced encoder-decoder network for autoisp

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:43.189221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 29d39c95-867c-4dc1-a3b4-f9d406c7da20 · outbound

This paper cites Rawhdr: High dynamic range image reconstruction from a single raw im- age.

RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report Rawhdr: High dynamic range image reconstruction from a single raw im- age

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:33:42.991092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers