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Paper Citation Record · LEDGER

Reversing Flow for Image Restoration

As of 17 August 2026, this Paper Citation Record lists 100 of 135 outbound references and 0 inbound Pith citation observations for arXiv:2506.16961.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.16961 v1

Coverage vector

measured 100 of 135 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:21:35.091249Z

measured 100 of 100 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

100 of 135 outbound references displayed

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  • verified fuzzy26
  • unresolved74
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Outbound references

Observation 1b8fa1e9-04aa-4620-a70b-6f2bf94e67d9 · outbound

This paper cites A high-quality denoising dataset for smartphone cameras.

Reversing Flow for Image Restoration A high-quality denoising dataset for smartphone cameras

Reference 1

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Observation 292fffb3-7232-4bf5-b567-303d82615018 · outbound

This paper cites Defocus de- blurring using dual-pixel data.

Reversing Flow for Image Restoration Defocus de- blurring using dual-pixel data

Reference 2

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Observation 607d53a8-793d-4ae9-899c-e122f8a7b1c5 · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Dataset and study.

Reversing Flow for Image Restoration Ntire 2017 challenge on single image super-resolution: Dataset and study

Reference 3

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Observation 3a6ea731-3183-46d2-998c-9a8a4760aebd · outbound

This paper cites Dense-haze: A benchmark for image dehazing with dense-haze and haze-free images.

Reversing Flow for Image Restoration Dense-haze: A benchmark for image dehazing with dense-haze and haze-free images

Reference 4

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Observation 5473f44b-3596-4e58-befa-08a6bc11bab0 · outbound

This paper cites Nh-haze: An image dehazing benchmark with non- homogeneous hazy and haze-free images.

Reversing Flow for Image Restoration Nh-haze: An image dehazing benchmark with non- homogeneous hazy and haze-free images

Reference 5

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Observation 16da9acf-fa7b-4849-87c3-d5fd7811ccba · outbound

This paper cites Contour detection and hierarchical image segmentation.IEEE transactions on pattern analysis and machine intelligence, 33(5):898–916, 2010.

Reversing Flow for Image Restoration Contour detection and hierarchical image segmentation.IEEE transactions on pattern analysis and machine intelligence, 33(5):898–916, 2010

Reference 6

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Observation 6f4cd16a-1f2f-45e4-bcab-197f5bc15c51 · outbound

This paper cites Wasserstein generative adversarial networks.

Reversing Flow for Image Restoration Wasserstein generative adversarial networks

Reference 7

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Observation 83ddb01a-01ad-40a5-b9e7-bdf11d3a9950 · outbound

This paper cites Self-guided image dehazing using progressive feature fu- sion.IEEE Transactions on Image Processing, 31:1217– 1229, 2022.

Reversing Flow for Image Restoration Self-guided image dehazing using progressive feature fu- sion.IEEE Transactions on Image Processing, 31:1217– 1229, 2022

Reference 8

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Observation 14f501ee-c689-4a1d-9bdd-8f6337de72b2 · outbound

This paper cites Digital image restoration.IEEE signal processing magazine, 14(2):24– 41, 1997.

Reversing Flow for Image Restoration Digital image restoration.IEEE signal processing magazine, 14(2):24– 41, 1997

Reference 9

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Observation 707b23b0-96a1-44eb-874e-67d150dc43e0 · outbound

This paper cites Cold diffusion: Inverting arbitrary im- age transforms without noise.Advances in Neural Informa- tion Processing Systems, 36:41259–41282, 2023.

Reversing Flow for Image Restoration Cold diffusion: Inverting arbitrary im- age transforms without noise.Advances in Neural Informa- tion Processing Systems, 36:41259–41282, 2023

Reference 10

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Observation 6829ce68-eabb-4acf-b73e-fd0b193d2474 · outbound

This paper cites An intuitive proof of the data processing inequality.

Reversing Flow for Image Restoration An intuitive proof of the data processing inequality

Reference 11

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Observation 609cfcc2-6f54-4155-8dfd-60839a2dad5e · outbound

This paper cites The perception-distortion tradeoff.

Reversing Flow for Image Restoration The perception-distortion tradeoff

Reference 12

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Observation cfbf2cb0-1687-45b8-a391-44561e1d78f0 · outbound

This paper cites Dehazenet: An end-to-end system for single image haze removal.IEEE transactions on image process- ing, 25(11):5187–5198, 2016.

Reversing Flow for Image Restoration Dehazenet: An end-to-end system for single image haze removal.IEEE transactions on image process- ing, 25(11):5187–5198, 2016

Reference 13

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Observation 26a00edb-41c3-42af-a38f-7f4c01b4ad27 · outbound

This paper cites Simple baselines for image restoration.

Reversing Flow for Image Restoration Simple baselines for image restoration

Reference 14

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Observation 257a7c13-5d3b-4aa5-b0e7-dd9a77ea8b9a · outbound

This paper cites Neural ordinary differential equa- tions.Advances in neural information processing systems, 31, 2018.

Reversing Flow for Image Restoration Neural ordinary differential equa- tions.Advances in neural information processing systems, 31, 2018

Reference 15

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Observation d4d6ecba-1a2e-49be-a675-acfc68f016c5 · outbound

This paper cites Jstasr: Joint size and transparency- aware snow removal algorithm based on modified par- tial convolution and veiling effect removal.

Reversing Flow for Image Restoration Jstasr: Joint size and transparency- aware snow removal algorithm based on modified par- tial convolution and veiling effect removal

Reference 16

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Observation 5e9038f6-242d-4420-bf03-1590f9438038 · outbound

This paper cites Learning a sparse transformer network for effective image deraining.

Reversing Flow for Image Restoration Learning a sparse transformer network for effective image deraining

Reference 17

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Observation e5731d3e-1147-4749-b7df-a743281189f5 · outbound

This paper cites Rethinking coarse-to-fine ap- proach in single image deblurring.

Reversing Flow for Image Restoration Rethinking coarse-to-fine ap- proach in single image deblurring

Reference 18

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Observation 9c7bb32f-06c1-4479-ac45-fe2e91000d3e · outbound

This paper cites Focal network for image restoration.

Reversing Flow for Image Restoration Focal network for image restoration

Reference 19

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Observation 374dc04f-0554-4d44-ac9e-47fcd78f48c8 · outbound

This paper cites Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration.

Reversing Flow for Image Restoration Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration

Reference 20

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Observation c902da1e-9c72-4da3-9ef2-6d638c1cdcae · outbound

This paper cites Detail- recovery image deraining via context aggregation networks.

Reversing Flow for Image Restoration Detail- recovery image deraining via context aggregation networks

Reference 21

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Observation a20454e7-f896-475e-ac3f-b1f9aac39cf4 · outbound

This paper cites Multi-scale boosted de- hazing network with dense feature fusion.

Reversing Flow for Image Restoration Multi-scale boosted de- hazing network with dense feature fusion

Reference 22

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Observation 7e48c6aa-05f8-443a-9c1e-32f6985cb937 · outbound

This paper cites Quantization guided jpeg artifact correction.

Reversing Flow for Image Restoration Quantization guided jpeg artifact correction

Reference 23

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Observation 8c7db254-1f34-4027-aac3-e7848441a68a · outbound

This paper cites Gener- ative diffusion prior for unified image restoration and en- hancement.

Reversing Flow for Image Restoration Gener- ative diffusion prior for unified image restoration and en- hancement

Reference 24

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Observation 4b8e5b94-eeb1-4aa5-a874-4a3267226490 · outbound

This paper cites Generative adversarial networks.Com- munications of the ACM, 63(11):139–144, 2020.

Reversing Flow for Image Restoration Generative adversarial networks.Com- munications of the ACM, 63(11):139–144, 2020

Reference 25

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Observation 8503b367-310d-422b-9811-fa7595d50c35 · outbound

This paper cites Improved training of wasserstein gans.Advances in neural information process- ing systems, 30, 2017.

Reversing Flow for Image Restoration Improved training of wasserstein gans.Advances in neural information process- ing systems, 30, 2017

Reference 26

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Observation 3165ef69-2701-421e-803d-097dd4bc820e · outbound

This paper cites Image dehazing transformer with transmission-aware 3d position embedding.

Reversing Flow for Image Restoration Image dehazing transformer with transmission-aware 3d position embedding

Reference 27

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Observation 2d7aac2c-4b00-4a2e-9eaa-47f0e232d9a7 · outbound

This paper cites From sky to the ground: A large-scale bench- mark and simple baseline towards real rain removal.

Reversing Flow for Image Restoration From sky to the ground: A large-scale bench- mark and simple baseline towards real rain removal

Reference 28

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Observation c6b74435-1eb8-4d96-b7e8-525c39cc3bfd · outbound

This paper cites Deep residual learning for image recognition.

Reversing Flow for Image Restoration Deep residual learning for image recognition

Reference 29

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Observation e7913b69-e2ed-4cc2-be69-3dc39fd2550a · outbound

This paper cites Generic image restoration with flow based priors.

Reversing Flow for Image Restoration Generic image restoration with flow based priors

Reference 30

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Observation d12a0458-6611-4b9e-8b05-fa6dda9dd29c · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural informa- tion processing systems, 33:6840–6851, 2020.

Reversing Flow for Image Restoration Denoising dif- fusion probabilistic models.Advances in neural informa- tion processing systems, 33:6840–6851, 2020

Reference 31

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Observation 9c97c0f9-ccb1-4865-a44f-7cab6c78ac07 · outbound

This paper cites Selective wavelet attention learning for single im- age deraining.International Journal of Computer Vision, 129(4):1282–1300, 2021.

Reversing Flow for Image Restoration Selective wavelet attention learning for single im- age deraining.International Journal of Computer Vision, 129(4):1282–1300, 2021

Reference 32

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Observation 420953c7-c4cb-4149-ab18-fa852e755e3b · outbound

This paper cites Arbitrary style transfer in real-time with adaptive instance normalization.

Reversing Flow for Image Restoration Arbitrary style transfer in real-time with adaptive instance normalization

Reference 33

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Observation 7dfadaa9-ba84-462d-b20a-95bb18b91526 · outbound

This paper cites Image-to-image translation with conditional adver- sarial networks.

Reversing Flow for Image Restoration Image-to-image translation with conditional adver- sarial networks

Reference 34

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Observation ec714f9d-10ef-4c25-8fee-2338e94a2703 · outbound

This paper cites Towards flex- ible blind jpeg artifacts removal.

Reversing Flow for Image Restoration Towards flex- ible blind jpeg artifacts removal

Reference 35

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Observation 00137470-73ac-43de-a5e9-45cd2469dae1 · outbound

This paper cites Rain- free and residue hand-in-hand: A progressive coupled net- work for real-time image deraining.IEEE Transactions on Image Processing, 30:7404–7418, 2021.

Reversing Flow for Image Restoration Rain- free and residue hand-in-hand: A progressive coupled net- work for real-time image deraining.IEEE Transactions on Image Processing, 30:7404–7418, 2021

Reference 36

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Observation 5fcbf42b-55ec-402d-99f1-877d4294d680 · outbound

This paper cites Edge-based defocus blur estimation with adaptive scale selection.IEEE Trans- actions on Image Processing, 27(3):1126–1137, 2017.

Reversing Flow for Image Restoration Edge-based defocus blur estimation with adaptive scale selection.IEEE Trans- actions on Image Processing, 27(3):1126–1137, 2017

Reference 37

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source=pdf_text observed=2026-08-15T19:21:34.871321Z digest=sha256:8183aa3455aa139a70fcf1bdace41830164c5cf5b8bc9991c5e2e89e70dcd223

Observation 3e9aa577-52ce-4280-be1f-eebe7f3791cd · outbound

This paper cites Denoising diffusion restoration models.Advances in Neural Information Processing Systems, 35:23593–23606,.

Reversing Flow for Image Restoration Denoising diffusion restoration models.Advances in Neural Information Processing Systems, 35:23593–23606,

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source=pdf_text observed=2026-08-15T19:21:34.874738Z digest=sha256:50f808d642ec0541719b29dfecc28e21872b2f718433a2abb086b8ae38231c43

Observation 5ec684db-e507-4f57-9fdb-18fbbdcfa75d · outbound

This paper cites Bigcolor: Colorization using a genera- tive color prior for natural images.

Reversing Flow for Image Restoration Bigcolor: Colorization using a genera- tive color prior for natural images

Reference 39

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source=pdf_text observed=2026-08-15T19:21:34.878599Z digest=sha256:7656ac04d7c00dd5d8e33255fc27621037c17e1b0cfc7b4fed0ca2c6ffa332d1

Observation 5db454cc-d156-4f40-a4b6-9f7b2a3a7ba6 · outbound

This paper cites Auto-Encoding Variational Bayes.

Reversing Flow for Image Restoration Auto-Encoding Variational Bayes

Reference 40

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source=pdf_text observed=2026-08-15T19:21:34.882205Z digest=sha256:e362a7bce75d12137f7bea6ddc5d108911ec14c77bb828dad077fa10be72fe46

Observation b293fc53-bb6b-4ce1-8ade-247d137233ea · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Reversing Flow for Image Restoration Adam: A Method for Stochastic Optimization

Reference 41

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source=pdf_text observed=2026-08-15T19:21:34.885867Z digest=sha256:15ea9b30adda37bd53d469805316dab738cea1605bae0df89415b6eafa762574

Observation b760ff1f-5254-4798-bcd7-9aa57fe8b082 · outbound

This paper cites Glow: Generative flow with invertible 1x1 convolutions.Advances in neural information processing systems, 31, 2018.

Reversing Flow for Image Restoration Glow: Generative flow with invertible 1x1 convolutions.Advances in neural information processing systems, 31, 2018

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source=pdf_text observed=2026-08-15T19:21:34.889491Z digest=sha256:63c0550ed0e5b2ac30c34c3580cbf03b60434c58632e880d0b9a83896dcb57e6

Observation 7694d340-7de1-4134-abf6-5961ba1f8179 · outbound

This paper cites Estimating mutual information.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 69(6): 066138, 2004.

Reversing Flow for Image Restoration Estimating mutual information.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 69(6): 066138, 2004

Reference 43

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source=pdf_text observed=2026-08-15T19:21:34.893835Z digest=sha256:a35b62d7dea28f5a5bf0bc36eaa746701f62c8dd9f5c2135cef3367dd5eee335

Observation 60cf6269-f485-43ca-89e8-542127982b99 · outbound

This paper cites Photo-realistic single image super-resolution using a gener- ative adversarial network.

Reversing Flow for Image Restoration Photo-realistic single image super-resolution using a gener- ative adversarial network

Reference 44

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source=pdf_text observed=2026-08-15T19:21:34.897303Z digest=sha256:3ae25404bde765e03aaa610c2a7df66a5c996c992af1be8e21660f5f06c9b388

Observation 6f78f245-23a7-4b28-bc48-dc6244200b8b · outbound

This paper cites Deep defocus map estimation using domain adapta- tion.

Reversing Flow for Image Restoration Deep defocus map estimation using domain adapta- tion

Reference 45

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source=pdf_text observed=2026-08-15T19:21:34.900893Z digest=sha256:6bc09e3abf96354a031a702c920ae135353e04694bc562a5958ffd663016eb79

Observation 12a29aa7-14ac-4675-ac14-0bd29f92d58e · outbound

This paper cites Iterative filter adaptive network for single image defocus deblurring.

Reversing Flow for Image Restoration Iterative filter adaptive network for single image defocus deblurring

Reference 46

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T19:21:34.904567Z digest=sha256:068829b8e36138063d9f6171733eb0b8486b1e105ac3fee7ac642be3ce52fc8c

Observation 482766fb-10cc-4c85-a5b9-235d444e8d99 · outbound

This paper cites Aod-net: All-in-one dehazing network.

Reversing Flow for Image Restoration Aod-net: All-in-one dehazing network

Reference 47

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T19:21:34.907996Z digest=sha256:5640debe1b29a2fc05ada6cd99bf0bb6475e4aca91ab85ed4e1ac4fb59a4dd7d

Observation 73f294ac-7045-430d-b05b-44a1cf34ba0e · outbound

This paper cites Srdiff: Single image super-resolution with diffusion probabilistic models.

Reversing Flow for Image Restoration Srdiff: Single image super-resolution with diffusion probabilistic models

Reference 48

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source=pdf_text observed=2026-08-15T19:21:34.911337Z digest=sha256:9d9265bcd82bb6d9c07f101378df941fc1b8cf97c3d730c135b82eed8afacc77

Observation 1fa58c86-9cef-421f-a939-cd02cf9e08b4 · outbound

This paper cites Heavy rain image restoration: Integrating physics model and conditional adversarial learning.

Reversing Flow for Image Restoration Heavy rain image restoration: Integrating physics model and conditional adversarial learning

Reference 49

Resolution
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no resolver link, observed 2026-08-15T19:21:34.914558Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T19:21:34.914558Z digest=sha256:429df50b7090d0bb11980de5cd81e208af29ad888ec28ecbb78e60d271e346cb

Observation 988ac89a-24c9-4859-be8c-6a32720674aa · outbound

This paper cites Recurrent squeeze-and-excitation context aggre- gation net for single image deraining.

Reversing Flow for Image Restoration Recurrent squeeze-and-excitation context aggre- gation net for single image deraining

Reference 50

Resolution
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source=pdf_text observed=2026-08-15T19:21:34.918069Z digest=sha256:d674445e875b903e4dd806b2e54e8a71ec96b1df4720e319ca03874c9a81c8ea

Observation 714251e1-ff08-49c9-a45d-b9a33b21bb92 · outbound

This paper cites DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior.

Reversing Flow for Image Restoration DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

Reference 51

Resolution
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source=pdf_text observed=2026-08-15T19:21:34.921735Z digest=sha256:6fad866483972446c9c730fd46664bf61c65e817bf810ccce76ac9827aa6dbcb

Observation 98b97269-a066-4c13-a6bd-65f0a76378c3 · outbound

This paper cites Catch missing details: Image reconstruction with frequency augmented variational autoencoder.

Reversing Flow for Image Restoration Catch missing details: Image reconstruction with frequency augmented variational autoencoder

Reference 52

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source=pdf_text observed=2026-08-15T19:21:34.924737Z digest=sha256:6c4ab8f88857535d9574226b03ab04acf81dc675f0a2c26decf87cb6dc6f3f8b

Observation f92508da-9d80-42d2-a0ad-1a1edbba755f · outbound

This paper cites Unsupervised image denoising in real-world scenarios via self-collaboration parallel generative adversarial branches.

Reversing Flow for Image Restoration Unsupervised image denoising in real-world scenarios via self-collaboration parallel generative adversarial branches

Reference 53

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source=pdf_text observed=2026-08-15T19:21:34.927630Z digest=sha256:7c46a82100e805e00a7bf6a3c49dcaba07433f7dcd42a36c0928bcc6df001fc4

Observation 2d1267ab-3cda-4e17-8a3b-a27f7222905d · outbound

This paper cites Flow Matching for Generative Modeling.

Reversing Flow for Image Restoration Flow Matching for Generative Modeling

Reference 54

Resolution
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source=pdf_text observed=2026-08-15T19:21:34.930769Z digest=sha256:90516b6a448b670fb57dc589ff262558ebb13f9d5cfd78608fbd35bf7d6f58f5

Observation f7a6f932-e1ff-4f44-9754-0409f8e6e0c3 · outbound

This paper cites I$^2$SB: Image-to-Image Schr\"odinger Bridge.

Reversing Flow for Image Restoration I$^2$SB: Image-to-Image Schr\"odinger Bridge

Reference 55

Resolution
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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T19:21:34.933943Z digest=sha256:3221ca2ce20934c4f4bd8e66a316a7385747e22dedcb47455cc5a377e5ecbc45

Observation 6e9d04dc-3f89-426f-8ffa-424b4a20352c · outbound

This paper cites Structure matters: Tackling the semantic dis- crepancy in diffusion models for image inpainting.

Reversing Flow for Image Restoration Structure matters: Tackling the semantic dis- crepancy in diffusion models for image inpainting

Reference 56

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source=pdf_text observed=2026-08-15T19:21:34.937287Z digest=sha256:cdbf5061b00b7a7f61b6dc57d77c794097451424114d7d76f6f31ba8bb012179

Observation b816b116-818d-4453-bd1a-78a3addbfb2b · outbound

This paper cites Residual denoising diffu- sion models.

Reversing Flow for Image Restoration Residual denoising diffu- sion models

Reference 57

Resolution
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source=pdf_text observed=2026-08-15T19:21:34.940119Z digest=sha256:d633eccc45ae290401bdeda1a2a4d5175c917e2413ec664dbdebccec2e7783d5

Observation 43fc821a-8be2-45c1-8d32-4c98052f91cc · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Reversing Flow for Image Restoration Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 58

Resolution
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source=pdf_text observed=2026-08-15T19:21:34.943657Z digest=sha256:d27f1ec2a9ec5ba6c5a10be6b0bbe8a7d556e6f2ba5b80b9fd0d14160f6b7db0

Observation a4ce1e03-ed3f-4fa1-8a1f-cec1b179bf21 · outbound

This paper cites Diff-plugin: Revitalizing details for diffusion-based low-level tasks.

Reversing Flow for Image Restoration Diff-plugin: Revitalizing details for diffusion-based low-level tasks

Reference 59

Resolution
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source=pdf_text observed=2026-08-15T19:21:34.947793Z digest=sha256:5fc217a9bacbd65c16f176845dd37775148733c4cca2a1ab75629bed95e02464

Observation 53d9c5f6-430c-4c68-8833-100e8ce28352 · outbound

This paper cites Desnownet: Context-aware deep network for snow removal.IEEE Transactions on Image Processing, 27(6): 3064–3073, 2018.

Reversing Flow for Image Restoration Desnownet: Context-aware deep network for snow removal.IEEE Transactions on Image Processing, 27(6): 3064–3073, 2018

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source=pdf_text observed=2026-08-15T19:21:34.951452Z digest=sha256:9584eab3b1bc99ab9bb0afc62644e7bdd49b058234d4bb51be81f096f51126dd

Observation 950e79ac-357a-4e74-942c-234fa814d567 · outbound

This paper cites Decoupled Weight Decay Regularization.

Reversing Flow for Image Restoration Decoupled Weight Decay Regularization

Reference 61

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source=pdf_text observed=2026-08-15T19:21:34.955293Z digest=sha256:2b32463edc413d1c98cd15357073daa78c791a2d240c3fb40df3d8778e7fe6da

Observation f0c6acf0-bf59-4424-be36-08c676251027 · outbound

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

Reversing Flow for Image Restoration SGDR: Stochastic Gradient Descent with Warm Restarts

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source=pdf_text observed=2026-08-15T19:21:34.958879Z digest=sha256:20328fbd751cee84122f10b11c497ee23faca0b194c82fea521000bd08239a48

Observation e5fa4044-f698-483c-9d5e-4b73e741eb82 · outbound

This paper cites Normalizing flow as a flexi- ble fidelity objective for photo-realistic super-resolution.

Reversing Flow for Image Restoration Normalizing flow as a flexi- ble fidelity objective for photo-realistic super-resolution

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source=pdf_text observed=2026-08-15T19:21:34.962464Z digest=sha256:7cc5a057fa71013af93ef43c05b2b320e746a7e47eeb7fb2a3ae597d25453452

Observation 71d8a9f6-3666-4e7d-a0ca-0138bb7b5f75 · outbound

This paper cites Image Restoration with Mean-Reverting Stochastic Differential Equations.

Reversing Flow for Image Restoration Image Restoration with Mean-Reverting Stochastic Differential Equations

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source=pdf_text observed=2026-08-15T19:21:34.966036Z digest=sha256:29ab7a586381bf5b395d3114f2a20cbaa2179a9155d813dafb6818152ea8bfd8

Observation 28ea34ae-2eb3-4ca8-8afc-cb7e9ff45c70 · outbound

This paper cites Refusion: Enabling large- size realistic image restoration with latent-space diffusion models.

Reversing Flow for Image Restoration Refusion: Enabling large- size realistic image restoration with latent-space diffusion models

Reference 65

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source=pdf_text observed=2026-08-15T19:21:34.969655Z digest=sha256:b9cabaee56ef56bcc94694fa010d750fa1bee66b09afbe024bf739552da687b8

Observation 5e6e4153-9f22-42eb-823b-51c08283ea2b · outbound

This paper cites Sen- sitivity decouple learning for image compression artifacts reduction.IEEE Transactions on Image Processing, 2024.

Reversing Flow for Image Restoration Sen- sitivity decouple learning for image compression artifacts reduction.IEEE Transactions on Image Processing, 2024

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source=pdf_text observed=2026-08-15T19:21:34.973571Z digest=sha256:765df1367a1af18721707f478c87452a587164ded95093c3d0aa1873310d79a6

Observation 943e7e9d-4368-4b4b-a665-db49132ede5e · outbound

This paper cites Least squares gen- erative adversarial networks.

Reversing Flow for Image Restoration Least squares gen- erative adversarial networks

Reference 67

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verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.185825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:34.977282Z digest=sha256:7a4fd37c157e487aac03f16814377afc721edff4f3921791b77132ad44b821e9

Observation 87f900d8-3c9d-40d8-adb5-23f3d342f6b8 · outbound

This paper cites Intriguing Findings of Frequency Selection for Image Deblurring.

Reversing Flow for Image Restoration Intriguing Findings of Frequency Selection for Image Deblurring

Reference 68

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source=pdf_text observed=2026-08-15T19:21:34.980731Z digest=sha256:b09f2ccae7bc07900296939e25372ab38fe828e9c5e5f47a13f8f25e20152c56

Observation 8b6a82b7-91ae-4260-930c-28cd8b62e7e5 · outbound

This paper cites A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics.

Reversing Flow for Image Restoration A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.175347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:34.984634Z digest=sha256:f368361f962d1d0ae3d7aabbf747fe756eeb1239bc28eb417ab075dbec08ee86

Observation ae3becce-eb35-4ac9-98e1-dceff4aae8e5 · outbound

This paper cites Conditional Generative Adversarial Nets.

Reversing Flow for Image Restoration Conditional Generative Adversarial Nets

Reference 70

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source=pdf_text observed=2026-08-15T19:21:34.988263Z digest=sha256:0e5052265f95d008879e37673288ebc0073d17e0495430708e4eded67fa98f27

Observation 57340eac-343a-43f8-8c7a-60aeefb5b29e · outbound

This paper cites Noisier2noise: Learning to denoise from unpaired noisy data.

Reversing Flow for Image Restoration Noisier2noise: Learning to denoise from unpaired noisy data

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.164179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:34.992122Z digest=sha256:c91c01e54916d1c7fac58c7214963c89554aca02c660b4670648dd61268c781d

Observation a6545c56-86d9-4e8f-a001-32286d102ec3 · outbound

This paper cites Dynamic at- tentive graph learning for image restoration.

Reversing Flow for Image Restoration Dynamic at- tentive graph learning for image restoration

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.153036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:34.995711Z digest=sha256:357f6907ed6cb4ed0593e692f1449a1918cdcd07c15d9e438d6093f80207d88b

Observation 989077ea-9283-448c-8c9e-831e07aa4189 · outbound

This paper cites T2i-adapter: Learn- ing adapters to dig out more controllable ability for text-to- image diffusion models.

Reversing Flow for Image Restoration T2i-adapter: Learn- ing adapters to dig out more controllable ability for text-to- image diffusion models

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.142231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:34.999398Z digest=sha256:e7b3263cbf5c26cc6bb385d8dddc8634bf3ece72e9f52ffd505f06e100523bad

Observation 4b0babe8-709a-4f85-b257-8ee2e668cb8b · outbound

This paper cites CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution.

Reversing Flow for Image Restoration CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:21:35.003025Z digest=sha256:4006dcb196d17374e8884009ce2d8ac96434e58bf476624dd99ef00527965697

Observation c0397dff-2d25-4412-95d1-43d1c7c30784 · outbound

This paper cites Ot-flow: Fast and accurate continuous normal- izing flows via optimal transport.

Reversing Flow for Image Restoration Ot-flow: Fast and accurate continuous normal- izing flows via optimal transport

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.131073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.007119Z digest=sha256:c7ba1a48cc76ea5670f44f56ecedee50fc2cfa0e37c5e8ac7aa62261dfca43f9

Observation b4bec076-f946-4952-8056-d449636854e2 · outbound

This paper cites Restoring vision in adverse weather conditions with patch-based denoising diffusion models.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8):10346–10357, 2023.

Reversing Flow for Image Restoration Restoring vision in adverse weather conditions with patch-based denoising diffusion models.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8):10346–10357, 2023

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.119412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.010688Z digest=sha256:a8e0b0e693627c3655ac5edbb7f709be46f7c3c99827b854c77638a5f44e7b5b

Observation 8962cb72-3401-4d33-94de-6be22f6260e8 · outbound

This paper cites Exploiting deep genera- tive prior for versatile image restoration and manipulation.

Reversing Flow for Image Restoration Exploiting deep genera- tive prior for versatile image restoration and manipulation

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.109981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.014264Z digest=sha256:878642f3bde509f323c5209f438ff0732b15081add98f46693385ee542ebdbd6

Observation ebaa231c-973c-4cf8-aec2-b6a6f9518076 · outbound

This paper cites Normalizing flows for probabilistic modeling and infer- ence.Journal of Machine Learning Research, 22(57):1–64,.

Reversing Flow for Image Restoration Normalizing flows for probabilistic modeling and infer- ence.Journal of Machine Learning Research, 22(57):1–64,

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.100197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.017749Z digest=sha256:d354c9fdc16a0df69c511bc653b843a107f3f61bdd787ddf7b838b86b226434e

Observation 3562fe91-862f-485c-997b-ee848f068f11 · outbound

This paper cites Ffa-net: Feature fusion attention network for single image dehazing.

Reversing Flow for Image Restoration Ffa-net: Feature fusion attention network for single image dehazing

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.090516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.021246Z digest=sha256:cc388d0ed9b708c02890d116589f11c3fb39f54acd8964c77d8941959e07a1fc

Observation 525f8c8a-c7df-4fee-b9b5-21eaa7bab082 · outbound

This paper cites Mb-taylorformer: Multi-branch efficient transformer expanded by taylor formula for im- age dehazing.

Reversing Flow for Image Restoration Mb-taylorformer: Multi-branch efficient transformer expanded by taylor formula for im- age dehazing

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.079768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.024860Z digest=sha256:f199ca68c1d801f93fbd08e17a1f8595de9e92b61356e276189674c1fc12b3f1

Observation e8f29925-d96e-473a-9085-7e240ad09021 · outbound

This paper cites Adaptive consistency prior based deep network for image denoising.

Reversing Flow for Image Restoration Adaptive consistency prior based deep network for image denoising

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-15T19:21:35.028618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:21:35.028618Z digest=sha256:2b57b8cbeeb9b5c771cacc1b74194155af3b9647cf37781aa1b7e194ec374aeb

Observation b1103532-9f10-4f24-b325-31892d2c4a92 · outbound

This paper cites Progressive image deraining net- works: A better and simpler baseline.

Reversing Flow for Image Restoration Progressive image deraining net- works: A better and simpler baseline

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.061598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.032143Z digest=sha256:720a9d29ab8f7151bbe442d40217002cd93f4965b0dccc6aee2c851c8784ad40

Observation 12bbe418-b832-48c4-b670-9815aa6172aa · outbound

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

Reversing Flow for Image Restoration U- net: Convolutional networks for biomedical image segmen- tation

Reference 83

Resolution
unresolved
no resolver link, observed 2026-08-15T19:21:35.035853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:21:35.035853Z digest=sha256:e29b69c9d62eaa12b07cab0a4137075a3fd41cf640319a6f45190195dc18df15

Observation 1252388f-02c7-4956-a2a3-000ee657856c · outbound

This paper cites Learning to deblur using light field generated and real de- focus images.

Reversing Flow for Image Restoration Learning to deblur using light field generated and real de- focus images

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.041958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.038863Z digest=sha256:363d2888d229f4e4675704cd3be540f8bc41508d563bafc21c528192275ae564

Observation bfc50c6c-dbbf-4555-9a7d-0641b315e732 · outbound

This paper cites Improved techniques for training gans.Advances in neural information process- ing systems, 29, 2016.

Reversing Flow for Image Restoration Improved techniques for training gans.Advances in neural information process- ing systems, 29, 2016

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.030267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.041729Z digest=sha256:f7164078bea31131ee4fc9fd3db40016b3475906c122586f7a1da95d7058035c

Observation 7b69af0c-ac78-4690-b0fe-13e7cd85f894 · outbound

This paper cites Resdiff: Combining cnn and diffusion model for image super-resolution.

Reversing Flow for Image Restoration Resdiff: Combining cnn and diffusion model for image super-resolution

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.018313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.044723Z digest=sha256:de43953e249e05f07a2cba7682fa9ddec6671213ac26c9c019f556dfc45b35ca

Observation bd6116dc-7c66-417e-83a6-88e5644ad560 · outbound

This paper cites Live image quality assessment database release 2.http://live.

Reversing Flow for Image Restoration Live image quality assessment database release 2.http://live

Reference 87

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:36.007402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.047617Z digest=sha256:3c17fd123d98c859827b2bab7e988ce347a6419f088d29299c2195eb5b5076ff

Observation 161da953-d15a-4dcc-b305-62318e6716e8 · outbound

This paper cites Just noticeable defocus blur detection and estimation.

Reversing Flow for Image Restoration Just noticeable defocus blur detection and estimation

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:35.877558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.050630Z digest=sha256:72a44732d8c6f6b621766bf47c04c39b2da6a14f74f619c0dddaed6ac2401ac4

Observation 1e905423-7b60-4474-892e-9c7e3f2f4702 · outbound

This paper cites Resfusion: Denoising diffusion probabilistic models for image restoration based on prior residual noise.

Reversing Flow for Image Restoration Resfusion: Denoising diffusion probabilistic models for image restoration based on prior residual noise

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:35.866549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.053674Z digest=sha256:954ca721456deb6a8586fd6300667b2782ae464ad4e6780171834887c6bf5d05

Observation d402ebb7-345a-47ae-99f8-ee474e84e086 · outbound

This paper cites Variational deep image restoration.IEEE Transactions on Image Processing, 31: 4363–4376, 2022.

Reversing Flow for Image Restoration Variational deep image restoration.IEEE Transactions on Image Processing, 31: 4363–4376, 2022

Reference 90

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:35.855857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.056571Z digest=sha256:c1c457991a152805d77f6f362493396ef0441cf7378474c7c6bd7fce4fbc6f04

Observation 10e0ffc9-3391-41c9-b1a8-a69197dff213 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Reversing Flow for Image Restoration Deep unsupervised learning using nonequilibrium thermodynamics

Reference 91

Resolution
unresolved
no resolver link, observed 2026-08-15T19:21:35.059834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:21:35.059834Z digest=sha256:21a4c7b67df0df016ccf4c3cc5135fb9fa1e4b9ed46e284e71659e8abb4789c6

Observation c939c5e4-809e-47d0-89fb-44f9c47672ff · outbound

This paper cites Single image defocus deblurring using kernel-sharing parallel atrous convolutions.

Reversing Flow for Image Restoration Single image defocus deblurring using kernel-sharing parallel atrous convolutions

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:35.838611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.062893Z digest=sha256:5b4b4e056675d0ea18a31b2736fc636c5bcf2539b2d04e70ea5414a781491810

Observation d1da3762-b8ef-49d0-b1aa-8c99952badbb · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Reversing Flow for Image Restoration Score-Based Generative Modeling through Stochastic Differential Equations

Reference 93

Resolution
unresolved
no resolver link, observed 2026-08-15T19:21:35.065929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:21:35.065929Z digest=sha256:12b9e9df398b020353fae82fccc500b4f91226766b6c8d1d20d861502d38b461

Observation 35296ca0-c6ac-4308-9333-7a400fbc1796 · outbound

This paper cites Transweather: Transformer-based restoration of im- ages degraded by adverse weather conditions.

Reversing Flow for Image Restoration Transweather: Transformer-based restoration of im- ages degraded by adverse weather conditions

Reference 94

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:35.827459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.069644Z digest=sha256:644125909e9f203bd631a8f50054f5652bdd2911940b8484bac58cb6215e728f

Observation 60603be5-fb58-4718-9c28-7568c6f2e98d · outbound

This paper cites Spatial attentive single-image deraining with a high quality real rain dataset.

Reversing Flow for Image Restoration Spatial attentive single-image deraining with a high quality real rain dataset

Reference 95

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:35.817045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.073316Z digest=sha256:80828d3d27a80fdd73c627b459af0c2d09a0485f5705c0f909f99f2798080b7c

Observation da943c52-6528-47f5-988d-fadfb976ad3b · outbound

This paper cites Esrgan: En- hanced super-resolution generative adversarial networks.

Reversing Flow for Image Restoration Esrgan: En- hanced super-resolution generative adversarial networks

Reference 96

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:35.806917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.076886Z digest=sha256:4329e46eaf58d3e217b0ac22b40103650c5efe2509ed39fa9539b9ae263a44df

Observation ba5fdde7-d643-4cfd-b099-11d7c981d1f8 · outbound

This paper cites To- wards real-world blind face restoration with generative fa- cial prior.

Reversing Flow for Image Restoration To- wards real-world blind face restoration with generative fa- cial prior

Reference 97

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:35.796139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.080632Z digest=sha256:46ceb5fd2d29a5ec0ee49984afc7ae2e3aa17d17a987e84641d4948ac3e12bb2

Observation dee0b8ea-0bbc-4242-94db-79fb4205a11a · outbound

This paper cites Real-esrgan: Training real-world blind super-resolution with pure synthetic data.

Reversing Flow for Image Restoration Real-esrgan: Training real-world blind super-resolution with pure synthetic data

Reference 98

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:35.785914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.084084Z digest=sha256:4da7ddae6c13c397d0deb216dc3b170945d8e081ccb28f1f3f26f3292820047d

Observation 5a7830de-49a1-473c-b4c8-2eab85edb164 · outbound

This paper cites Low-light image enhancement with normalizing flow.

Reversing Flow for Image Restoration Low-light image enhancement with normalizing flow

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:21:35.775804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T19:21:35.087930Z digest=sha256:6fefe0b8756e989718641a93dd475e7a88c8704a09b53e51f5eb9fc63da42e73

Observation 8517f544-1aae-4778-8d2f-32fa992da5f5 · outbound

This paper cites Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model.

Reversing Flow for Image Restoration Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model

Reference 100

Resolution
unresolved
no resolver link, observed 2026-08-15T19:21:35.091249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:21:35.091249Z digest=sha256:0203b91c1bdba6e2f08e771a83afe0fe8a3e20ee10c1991913dd5a0fa46f53cf

Pith citing papers

No inbound Pith citation observations are available.