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Paper Citation Record · LEDGER

IRPO: Boosting Image Restoration via Post-training GRPO

As of 19 August 2026, this Paper Citation Record lists 91 of 91 outbound references and 1 inbound Pith citation observation for arXiv:2512.00814.

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

pith.paper-citation-record.v1
2512.00814 v3

Coverage vector

measured 91 of 91 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:24:39.437108Z

measured 92 of 92 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T07:38:50.237882Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

91 of 91 outbound references displayed

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Outbound references

Observation 2f23b101-2dd6-44a4-9f67-696da170171f · outbound

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

IRPO: Boosting Image Restoration via Post-training GRPO A high-quality denoising dataset for smartphone cameras

Reference 1

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Observation 5572575a-6c74-4a1a-a653-a01ff33033a0 · outbound

This paper cites Contour detection and hierarchical image seg- mentation.TPAMI, 2010.

IRPO: Boosting Image Restoration via Post-training GRPO Contour detection and hierarchical image seg- mentation.TPAMI, 2010

Reference 2

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Observation 4f233de6-968d-4402-ad41-8892a8c76723 · outbound

This paper cites Not just streaks: Towards ground truth for single image derain- ing.

IRPO: Boosting Image Restoration via Post-training GRPO Not just streaks: Towards ground truth for single image derain- ing

Reference 3

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Observation 4909783c-d742-4d1a-9039-1b016d4a8d6a · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

IRPO: Boosting Image Restoration via Post-training GRPO Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 4

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Observation cfde42bf-4956-4698-a6dd-312df9c104e6 · outbound

This paper cites The perception-distortion tradeoff.

IRPO: Boosting Image Restoration via Post-training GRPO The perception-distortion tradeoff

Reference 5

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Observation 6afe147d-bb5f-40e1-85b3-2d0b586dfc0d · outbound

This paper cites Dehazenet: An end-to-end system for single image haze removal.TIP, 2016.

IRPO: Boosting Image Restoration via Post-training GRPO Dehazenet: An end-to-end system for single image haze removal.TIP, 2016

Reference 6

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source=pdf_text observed=2026-08-03T19:24:38.534541Z digest=sha256:16da4a2d05bea49c53c13cb8841c052cdc619301fbf1baa62ceecc5b403a3271

Observation feea8c42-a54f-4d9d-b45d-dba2ffec2436 · outbound

This paper cites Hinet: Half instance normalization network for image restoration.

IRPO: Boosting Image Restoration via Post-training GRPO Hinet: Half instance normalization network for image restoration

Reference 7

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Observation c39cf595-412a-450a-b4ef-a7ae6e64d275 · outbound

This paper cites Simple baselines for image restoration.

IRPO: Boosting Image Restoration via Post-training GRPO Simple baselines for image restoration

Reference 8

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source=pdf_text observed=2026-08-03T19:24:38.669070Z digest=sha256:eaa10a795a7e6518227ed2e0904278fa215493b824a4a2f209219712637f70e0

Observation ae647bd9-a64c-4318-b33c-9a7a4ae6c384 · outbound

This paper cites an unresolved cited work.

IRPO: Boosting Image Restoration via Post-training GRPO Unresolved cited work

Reference 9

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Observation 60736161-6a49-4168-bee6-5fa49cb1ebf8 · outbound

This paper cites AdaIR: Adap- tive all-in-one image restoration via frequency mining and modulation.

IRPO: Boosting Image Restoration via Post-training GRPO AdaIR: Adap- tive all-in-one image restoration via frequency mining and modulation

Reference 10

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source=pdf_text observed=2026-08-03T19:24:38.829784Z digest=sha256:38ab05818483a2f3a356c0e2c9fe7476626acf6a6716f8e16ac2013cb762f971

Observation 217771f7-d235-42b4-aa78-ef9fd1cc1374 · outbound

This paper cites Color image denoising via sparse 3d col- laborative filtering with grouping constraint in luminance- chrominance space.

IRPO: Boosting Image Restoration via Post-training GRPO Color image denoising via sparse 3d col- laborative filtering with grouping constraint in luminance- chrominance space

Reference 11

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source=pdf_text observed=2026-08-03T19:24:38.912046Z digest=sha256:0a72a4aea2cef43f42581f6ff0e3c9856b00739a26d75a5188cb6fca59144a96

Observation b221ce87-db04-455e-a6b3-0304171e78d3 · outbound

This paper cites Image super-resolution using deep convolutional net- works.IEEE transactions on pattern analysis and machine intelligence, 38(2):295–307, 2015.

IRPO: Boosting Image Restoration via Post-training GRPO Image super-resolution using deep convolutional net- works.IEEE transactions on pattern analysis and machine intelligence, 38(2):295–307, 2015

Reference 12

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source=pdf_text observed=2026-08-03T19:24:38.966234Z digest=sha256:ff984e8555d3a4d1ddfe3ffb1cc0c86c909140c0cd68dfb06b0e2428d2b3b99c

Observation fec16136-fe76-4d5e-8920-29cc6f8fed5b · outbound

This paper cites Fd-gan: Generative adversarial networks with fusion- discriminator for single image dehazing.

IRPO: Boosting Image Restoration via Post-training GRPO Fd-gan: Generative adversarial networks with fusion- discriminator for single image dehazing

Reference 13

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source=pdf_text observed=2026-08-03T19:24:39.046181Z digest=sha256:5d43c7bdad8c9ac5e7ad900a247ec6788e058b9f738a5138bf1b7a7136584fd8

Observation 99516f45-064b-491c-8435-65b856e1e48c · outbound

This paper cites A general decoupled learn- ing framework for parameterized image operators.TPAMI,.

IRPO: Boosting Image Restoration via Post-training GRPO A general decoupled learn- ing framework for parameterized image operators.TPAMI,

Reference 14

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source=pdf_text observed=2026-08-03T19:24:39.101365Z digest=sha256:227638e673b6475e13722184f384a60fa964e8f266aeaa09a0423eeb5385f174

Observation d857fd32-c8c1-482b-b4ca-cb11b79c0bbf · outbound

This paper cites Dy- namic scene deblurring with parameter selective sharing and nested skip connections.

IRPO: Boosting Image Restoration via Post-training GRPO Dy- namic scene deblurring with parameter selective sharing and nested skip connections

Reference 15

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source=pdf_text observed=2026-08-03T19:24:39.155783Z digest=sha256:bd5a8683c6787e93fd5732e7646075d0faaf700fb040b41ba6142a1e0fbab40c

Observation ed9af779-95b3-4d65-91f3-89ad7edd0086 · outbound

This paper cites Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering.

IRPO: Boosting Image Restoration via Post-training GRPO Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering

Reference 16

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source=pdf_text observed=2026-08-03T19:24:39.207564Z digest=sha256:4efbb8017ba6a99f76ec5a5079ee16ccbd7a5bf8ab28699f0bbe95d5a2f1ba68

Observation 9dfee3a1-7e99-44c9-afb0-998f9e7469f7 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

IRPO: Boosting Image Restoration via Post-training GRPO DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 17

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source=pdf_text observed=2026-08-03T19:24:39.232544Z digest=sha256:5821ad4252edf61340abf3ba3769562603fc816fcc04ad2b80b598e4f36f1886

Observation 3eeb0094-159c-4336-8656-eea6fdcf3958 · outbound

This paper cites Toward convolutional blind denoising of real pho- tographs.

IRPO: Boosting Image Restoration via Post-training GRPO Toward convolutional blind denoising of real pho- tographs

Reference 18

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source=pdf_text observed=2026-08-03T19:24:39.235720Z digest=sha256:9581247adcc0c554d8811d22a82e504e0f1403ee54bd354550d07b26d6199404

Observation 23512d5f-23e2-4032-9e38-ae17b5a7cdab · outbound

This paper cites Face super-resolution guided by 3d facial priors.

IRPO: Boosting Image Restoration via Post-training GRPO Face super-resolution guided by 3d facial priors

Reference 19

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source=pdf_text observed=2026-08-03T19:24:39.238981Z digest=sha256:68af50ea9e5344ab30a24304989b4612228d3b97f43b9703049e08cbb1e3b82e

Observation 6628b36f-500b-4fe5-be82-172807e1b07b · outbound

This paper cites Face restoration via plug-and-play 3d facial priors.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12):8910–8926, 2021.

IRPO: Boosting Image Restoration via Post-training GRPO Face restoration via plug-and-play 3d facial priors.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12):8910–8926, 2021

Reference 20

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source=pdf_text observed=2026-08-03T19:24:39.242111Z digest=sha256:88c53c423e9209c96dfc5c7c42c9d51b175a0a94f5223139654a90776327947b

Observation 571474a7-a983-48b8-8226-3240e007d558 · outbound

This paper cites Single image super-resolution from transformed self-exemplars.

IRPO: Boosting Image Restoration via Post-training GRPO Single image super-resolution from transformed self-exemplars

Reference 21

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source=pdf_text observed=2026-08-03T19:24:39.244753Z digest=sha256:513951c315875427533ab7f5cf230e38d79534bdd75ed643501b98437f783df5

Observation 90e2b5bb-80df-4329-970f-2f141a78e528 · outbound

This paper cites Hunyuan3d-omni: A unified framework for controllable generation of 3d assets.

IRPO: Boosting Image Restoration via Post-training GRPO Hunyuan3d-omni: A unified framework for controllable generation of 3d assets

Reference 22

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Observation ae444e51-b47e-4ecc-99a2-84262b80f6ae · outbound

This paper cites Loli-street: Bench- marking low-light image enhancement and beyond.

IRPO: Boosting Image Restoration via Post-training GRPO Loli-street: Bench- marking low-light image enhancement and beyond

Reference 23

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Observation cb1880f5-8d44-4726-841d-20291efb4537 · outbound

This paper cites OpenAI o1 System Card.

IRPO: Boosting Image Restoration via Post-training GRPO OpenAI o1 System Card

Reference 24

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source=pdf_text observed=2026-08-03T19:24:39.252897Z digest=sha256:6852e05e153316fcb6483b73fc9974e20b88b9517f3813675921ab5013c71732

Observation 16898427-0028-4a60-a018-f88e393b0630 · outbound

This paper cites Supervised learning of image restoration with convolutional networks.

IRPO: Boosting Image Restoration via Post-training GRPO Supervised learning of image restoration with convolutional networks

Reference 25

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source=pdf_text observed=2026-08-03T19:24:39.255708Z digest=sha256:e630606a32eb8e54e7c58f5fa9c99d6af0e54cf28db51f3f967328c1c4a4172d

Observation 5a70253b-6311-4c84-8cad-972c2e1161fc · outbound

This paper cites Sonic: Shifting focus to global au- dio perception in portrait animation.

IRPO: Boosting Image Restoration via Post-training GRPO Sonic: Shifting focus to global au- dio perception in portrait animation

Reference 26

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Observation a36b0dca-643d-48b3-ac50-fd4d50436f58 · outbound

This paper cites Multi-scale progressive fusion network for single image deraining.

IRPO: Boosting Image Restoration via Post-training GRPO Multi-scale progressive fusion network for single image deraining

Reference 27

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Observation a62abafa-08c6-478a-98e6-a861b7db506c · outbound

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

IRPO: Boosting Image Restoration via Post-training GRPO Perceptual losses for real-time style transfer and super-resolution

Reference 28

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Observation 5815d438-b195-45de-be6e-c6442623e4eb · outbound

This paper cites Can grpo boost complex mul- timodal table understanding? InProceedings of the 2025 Conference on Empirical Methods in Natural Language Pro- cessing, pages 12642–12655, 2025.

IRPO: Boosting Image Restoration via Post-training GRPO Can grpo boost complex mul- timodal table understanding? InProceedings of the 2025 Conference on Empirical Methods in Natural Language Pro- cessing, pages 12642–12655, 2025

Reference 29

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Observation 9bb1866f-e285-4f85-958c-e1db0a10bb99 · outbound

This paper cites A survey of post-training scaling in large language models.

IRPO: Boosting Image Restoration via Post-training GRPO A survey of post-training scaling in large language models

Reference 30

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source=pdf_text observed=2026-08-03T19:24:39.268584Z digest=sha256:c1c501015bd4fb89ad06d7be85efd7de7fc1ec33ee238f87235b581b6fdbd73e

Observation 46ff086a-b29b-451d-a5ed-e5b5277cf4b6 · outbound

This paper cites Noise2Noise: Learning Image Restoration without Clean Data.

IRPO: Boosting Image Restoration via Post-training GRPO Noise2Noise: Learning Image Restoration without Clean Data

Reference 31

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source=pdf_text observed=2026-08-03T19:24:39.271347Z digest=sha256:82b7739e23f38097ff971d44f7ebd241e386f777ea404deaf76dd8cd16170d11

Observation b4e234d8-651b-4a7e-b358-23e1cf831477 · outbound

This paper cites Benchmarking single- image dehazing and beyond.TIP, 2018.

IRPO: Boosting Image Restoration via Post-training GRPO Benchmarking single- image dehazing and beyond.TIP, 2018

Reference 32

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source=pdf_text observed=2026-08-03T19:24:39.274222Z digest=sha256:a563d855bb5031ed9462284e6627084ecba6606571db5dd9245b89d729b38a18

Observation 4d08bdde-df4d-4f7a-9751-c30c4f09ab02 · outbound

This paper cites All-in-one image restoration for unknown cor- ruption.

IRPO: Boosting Image Restoration via Post-training GRPO All-in-one image restoration for unknown cor- ruption

Reference 33

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Observation 44028547-7c83-42c6-8041-f21b7df47282 · outbound

This paper cites Real-world deep local motion deblur- ring.

IRPO: Boosting Image Restoration via Post-training GRPO Real-world deep local motion deblur- ring

Reference 34

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source=pdf_text observed=2026-08-03T19:24:39.279394Z digest=sha256:1f6f80a164c0d047f509ccfec3e77a3c620d017d2fa9a496a9eb50d371a6f3cb

Observation 22843845-0f3a-4030-a75e-976f4f12f2fc · outbound

This paper cites T2v- turbo-v2: Enhancing video generation model post-training through data, reward, and conditional guidance design.arXiv preprint arXiv:2410.05677, 2024.

IRPO: Boosting Image Restoration via Post-training GRPO T2v- turbo-v2: Enhancing video generation model post-training through data, reward, and conditional guidance design.arXiv preprint arXiv:2410.05677, 2024

Reference 35

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Observation c0c9923c-84e9-421a-a032-08fe25c9cb2d · outbound

This paper cites Swinir: Image restoration us- ing swin transformer.

IRPO: Boosting Image Restoration via Post-training GRPO Swinir: Image restoration us- ing swin transformer

Reference 36

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source=pdf_text observed=2026-08-03T19:24:39.284556Z digest=sha256:e06f43ad9feda42bb031e3a4e59726223b74e7e39e9ba2965e3c39deac272aeb

Observation 7b729df0-dea9-42fc-bb77-8f0eaefc776e · outbound

This paper cites Vrt: A video restoration transformer.IEEE Transactions on Image Processing, 33:2171–2182, 2024.

IRPO: Boosting Image Restoration via Post-training GRPO Vrt: A video restoration transformer.IEEE Transactions on Image Processing, 33:2171–2182, 2024

Reference 37

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source=pdf_text observed=2026-08-03T19:24:39.287635Z digest=sha256:4a846f8aa59dbf112fdb854098a9903d3b1f1811a9e6d73dfa3144a0219beb39

Observation 76c252c4-1828-4503-9b0b-4367180f5ea7 · outbound

This paper cites Vton-handfit: Virtual try-on for arbi- trary hand pose guided by hand priors embedding.

IRPO: Boosting Image Restoration via Post-training GRPO Vton-handfit: Virtual try-on for arbi- trary hand pose guided by hand priors embedding

Reference 38

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source=pdf_text observed=2026-08-03T19:24:39.290291Z digest=sha256:8421ace76c3ec368cab7a252581debbaeab6d6772d24c1202d41fe1c900de5a3

Observation fb03eca8-6161-40b6-be92-914c94e237e9 · outbound

This paper cites Diffusion adversarial post-training for one-step video generation.arXiv preprint arXiv:2501.08316,.

IRPO: Boosting Image Restoration via Post-training GRPO Diffusion adversarial post-training for one-step video generation.arXiv preprint arXiv:2501.08316,

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source=pdf_text observed=2026-08-03T19:24:39.292767Z digest=sha256:7a3587f6c426fa37028910fbec26848a2c2c6b0eff3f4ca865c143032ac332d4

Observation 7feb49c2-ddaf-42fc-be7d-640542e53845 · outbound

This paper cites Autoregressive adversarial post-training for real-time inter- active video generation.arXiv preprint arXiv:2506.09350,.

IRPO: Boosting Image Restoration via Post-training GRPO Autoregressive adversarial post-training for real-time inter- active video generation.arXiv preprint arXiv:2506.09350,

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source=pdf_text observed=2026-08-03T19:24:39.295640Z digest=sha256:cbee4a2f4a31c2745c25c1e2283ee5c77e00a3f2aa2420edd962da7aa092ad96

Observation 92f3335f-7d4a-421b-a7e6-c36c43c4a119 · outbound

This paper cites Tape: Task-agnostic prior embedding for image restoration.

IRPO: Boosting Image Restoration via Post-training GRPO Tape: Task-agnostic prior embedding for image restoration

Reference 41

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source=pdf_text observed=2026-08-03T19:24:39.298091Z digest=sha256:cd56b7f3347aca9be6f500582709f2f1102fabdba3be7e1c15f0b09be8f06af1

Observation 98c906bd-3048-4648-b010-9c03afa9d842 · outbound

This paper cites Moa-vr: A mixture- of-agents system towards all-in-one video restoration.arXiv preprint arXiv:2510.08508, 2025.

IRPO: Boosting Image Restoration via Post-training GRPO Moa-vr: A mixture- of-agents system towards all-in-one video restoration.arXiv preprint arXiv:2510.08508, 2025

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source=pdf_text observed=2026-08-03T19:24:39.300641Z digest=sha256:ea15c24f1f94abd6eb7d1a169f9f0362ee8b28e9c0b3734de03c434c245ba511

Observation 4f1a54a6-0afd-4e23-a383-795350605cf3 · outbound

This paper cites Two-stage mamba-based diffusion model for image restora- tion.Scientific Reports, 15(1):22265, 2025.

IRPO: Boosting Image Restoration via Post-training GRPO Two-stage mamba-based diffusion model for image restora- tion.Scientific Reports, 15(1):22265, 2025

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source=pdf_text observed=2026-08-03T19:24:39.303633Z digest=sha256:bc951896873f1383e728ecec29b8730b66f480e5be4fb514684d6dfb9a71c132

Observation 7922e5b3-4e0b-4298-8e41-375e18aa2f7a · outbound

This paper cites Post-training quantization for vision trans- former.Advances in Neural Information Processing Systems, 34:28092–28103, 2021.

IRPO: Boosting Image Restoration via Post-training GRPO Post-training quantization for vision trans- former.Advances in Neural Information Processing Systems, 34:28092–28103, 2021

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source=pdf_text observed=2026-08-03T19:24:39.306125Z digest=sha256:9206c74f35bd3ddabcb2b2ead4d3866699ebc0e97b38e73c511c127cad961f00

Observation 768c9bc7-ca53-4a09-96ee-643fdb8f9fcc · outbound

This paper cites Waterloo ex- ploration database: New challenges for image quality assess- ment models.TIP, 2016.

IRPO: Boosting Image Restoration via Post-training GRPO Waterloo ex- ploration database: New challenges for image quality assess- ment models.TIP, 2016

Reference 45

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source=pdf_text observed=2026-08-03T19:24:39.308906Z digest=sha256:13f018e7e5da86d48a7b8208c3fd3696cb3b5fbb3150f050da08b9f5c3f87b65

Observation b651bc9a-9266-4d96-ab0a-6cc503aae921 · outbound

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

IRPO: Boosting Image Restoration via Post-training GRPO A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

Reference 46

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source=pdf_text observed=2026-08-03T19:24:39.311376Z digest=sha256:b8787d1089acfff00efaba280594c182d7596d5430b2f6c3fa88e85ae803b08b

Observation 751f7cf0-e40e-46cf-9a69-09c384d2c183 · outbound

This paper cites Deep generalized unfolding networks for image restoration.

IRPO: Boosting Image Restoration via Post-training GRPO Deep generalized unfolding networks for image restoration

Reference 47

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source=pdf_text observed=2026-08-03T19:24:39.313929Z digest=sha256:a3f49ca45c8dd157d1bc5209bf0de76381d167d36582e3eda6ba92ef65b2360c

Observation 5fcf4bf7-687c-407e-b5dd-8a1ee2e9aa33 · outbound

This paper cites Deep multi-scale convolutional neural network for dynamic scene deblurring.

IRPO: Boosting Image Restoration via Post-training GRPO Deep multi-scale convolutional neural network for dynamic scene deblurring

Reference 48

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source=pdf_text observed=2026-08-03T19:24:39.316536Z digest=sha256:a759272baf0b0ddfbc1549682ad187cee50b8220d9e108de10d957ba2e771426

Observation 1a09e818-be8b-4774-a3d5-71633af190db · outbound

This paper cites Promptir: Prompting for all-in- one image restoration.NeurIPS, 2023.

IRPO: Boosting Image Restoration via Post-training GRPO Promptir: Prompting for all-in- one image restoration.NeurIPS, 2023

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source=pdf_text observed=2026-08-03T19:24:39.319042Z digest=sha256:f6f80d0fdb43be11fdf50143d6c98550b1eb8ac010ca6c28cf86f5784d8afce9

Observation 8f6d4493-0bc1-4a99-9994-6333f0c359cd · outbound

This paper cites VLN-R1: Vision-Language Navigation via Reinforcement Fine-Tuning.

IRPO: Boosting Image Restoration via Post-training GRPO VLN-R1: Vision-Language Navigation via Reinforcement Fine-Tuning

Reference 50

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source=pdf_text observed=2026-08-03T19:24:39.321499Z digest=sha256:f00f0b9e0de8966f73a0a00461b545a1f973fb8e58bcab032a7e6b99baaa1c4e

Observation f9e9a19b-17ea-4cec-b9b0-61dc61d4d9a3 · outbound

This paper cites En- hanced pix2pix dehazing network.

IRPO: Boosting Image Restoration via Post-training GRPO En- hanced pix2pix dehazing network

Reference 51

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source=pdf_text observed=2026-08-03T19:24:39.324672Z digest=sha256:ea3063de7b93a093c5e9a4e9bc3d88d8bb6da0bca878b3115b100f87cda4f5bb

Observation 9c7e288e-70c7-436b-ba8e-7c1cea19f9e4 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

IRPO: Boosting Image Restoration via Post-training GRPO Learning transferable visual models from natural language supervi- sion

Reference 52

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source=pdf_text observed=2026-08-03T19:24:39.327253Z digest=sha256:658df18d98616c8b54130bf33b5a5e3dd3ea266ae9ac9eca50833b373c7fa414

Observation 219231a8-be9c-4b23-ba40-a164ca47320d · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.Advances in neural information processing systems, 36:53728–53741, 2023.

IRPO: Boosting Image Restoration via Post-training GRPO Direct preference optimization: Your language model is secretly a reward model.Advances in neural information processing systems, 36:53728–53741, 2023

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source=pdf_text observed=2026-08-03T19:24:39.329871Z digest=sha256:9762b9215c0cbe37fa0193eaf3959b7696da91864df57f53a03459c66d8a771e

Observation 6fa7d36c-f232-4ca4-aaeb-da0b8d1bcf31 · outbound

This paper cites Single image dehazing via multi- scale convolutional neural networks.

IRPO: Boosting Image Restoration via Post-training GRPO Single image dehazing via multi- scale convolutional neural networks

Reference 54

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source=pdf_text observed=2026-08-03T19:24:39.332520Z digest=sha256:75e65474cdafeeb31235ea1962b64f55b52f1769210bad11eb1321005e5f9aa4

Observation 2323ba71-286c-4e37-95c4-b54b8f75c630 · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in neural in- formation processing systems, 35:25278–25294, 2022.

IRPO: Boosting Image Restoration via Post-training GRPO Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in neural in- formation processing systems, 35:25278–25294, 2022

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source=pdf_text observed=2026-08-03T19:24:39.335265Z digest=sha256:7bfe56ce6302adda683edbcb22851be9250642ab1ff61cef5ebc8d47e9eab146

Observation 747a5489-7f3c-4c55-8d00-5d6628b7e17c · outbound

This paper cites Post-training quantization on diffusion models.

IRPO: Boosting Image Restoration via Post-training GRPO Post-training quantization on diffusion models

Reference 56

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source=pdf_text observed=2026-08-03T19:24:39.337911Z digest=sha256:efaf282a8986970ebf31d431699ec5f10b0b210f538ba52cb0d3883a940077e3

Observation 62eb4948-55bc-4d9a-99e0-de4e55185b90 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

IRPO: Boosting Image Restoration via Post-training GRPO DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

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source=pdf_text observed=2026-08-03T19:24:39.340677Z digest=sha256:af9e36f94033f87efa116c6e0f9267814acdf5c719f22d3e5ae223662058b878

Observation f3914dc9-f482-44e9-96b5-5b231900b975 · outbound

This paper cites Fine-grained image quality assessment for per- ceptual image restoration.arXiv preprint arXiv:2508.14475,.

IRPO: Boosting Image Restoration via Post-training GRPO Fine-grained image quality assessment for per- ceptual image restoration.arXiv preprint arXiv:2508.14475,

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source=pdf_text observed=2026-08-03T19:24:39.343943Z digest=sha256:57a5d8ccdba921a47f3b4364745cd80ec09cfd7cdb5fb621f87a1bd215651150

Observation 81d81336-1112-4d5d-b41a-27eaec3a0852 · outbound

This paper cites Image de- noising using deep cnn with batch renormalization.Neural Networks, 2020.

IRPO: Boosting Image Restoration via Post-training GRPO Image de- noising using deep cnn with batch renormalization.Neural Networks, 2020

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source=pdf_text observed=2026-08-03T19:24:39.346709Z digest=sha256:d57659b5fa23c2760a9819a2457829c7cd4b1516c28d8b54fc3e703a5274eb38

Observation 3e3f37ad-2c6a-4abd-a980-19bfdf46a247 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

IRPO: Boosting Image Restoration via Post-training GRPO LLaMA: Open and Efficient Foundation Language Models

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source=pdf_text observed=2026-08-03T19:24:39.349405Z digest=sha256:4b167ae78ef65c5d91025f479a503be3bdcdf68cc75b57d3c906b940d0021d89

Observation 57dfbe95-4bdb-4dd8-8fbe-4882408549f7 · outbound

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

IRPO: Boosting Image Restoration via Post-training GRPO Transweather: Transformer-based restoration of im- ages degraded by adverse weather conditions

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source=pdf_text observed=2026-08-03T19:24:39.352263Z digest=sha256:6486fe9cea8ad05412f41120bcd22b43fd747ae71d514fdaed778b85c4c1012f

Observation 4b79ff92-5b99-4c9c-ac49-54c7d345094e · outbound

This paper cites Apisr: Anime production inspired real-world anime super-resolution.

IRPO: Boosting Image Restoration via Post-training GRPO Apisr: Anime production inspired real-world anime super-resolution

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source=pdf_text observed=2026-08-03T19:24:39.355031Z digest=sha256:a99f544b19084db4ec0d001f7f128106a9d7e87faadaae6124a15b733ed4b008

Observation d281c04e-1475-4c0b-8ca7-70ac309e07ae · outbound

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

IRPO: Boosting Image Restoration via Post-training GRPO Esrgan: En- hanced super-resolution generative adversarial networks

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source=pdf_text observed=2026-08-03T19:24:39.357610Z digest=sha256:bc5ecc5a22c056e51c3322fdd1993768c5950be3f67b71da55ee640ef385d184

Observation 1e050e86-86c3-4e09-ba22-b49d222beb4e · outbound

This paper cites Deep Retinex Decomposition for Low-Light Enhancement.

IRPO: Boosting Image Restoration via Post-training GRPO Deep Retinex Decomposition for Low-Light Enhancement

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source=pdf_text observed=2026-08-03T19:24:39.360170Z digest=sha256:9be1448403cd9907aa089c26881d4c970c6c935dc81e4d7e7c39056b1c8765c8

Observation 31796191-5a9b-46d5-8afe-d7a698f1d6c8 · outbound

This paper cites Boosting All-in-One Image Restoration via Self-Improved Privilege Learning.

IRPO: Boosting Image Restoration via Post-training GRPO Boosting All-in-One Image Restoration via Self-Improved Privilege Learning

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source=pdf_text observed=2026-08-03T19:24:39.363019Z digest=sha256:17b43561e0e3e6e368fcc7ae287ebd4c9ebd4ec5b2e330c9aecd7be27952e3df

Observation 720a601b-3427-4f42-af86-2c69295ee113 · outbound

This paper cites Cross-domain car detection model with integrated convolu- tional block attention mechanism.Image and Vision Com- puting, 140:104834, 2023.

IRPO: Boosting Image Restoration via Post-training GRPO Cross-domain car detection model with integrated convolu- tional block attention mechanism.Image and Vision Com- puting, 140:104834, 2023

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source=pdf_text observed=2026-08-03T19:24:39.365893Z digest=sha256:064f68ae792646d688bfdef7273506de80859a72236bccdb88a180cc2c0b1529

Observation 10fcfdd8-f0c0-45e5-8cb6-e37bd0542225 · outbound

This paper cites Joint rain detection and removal from a single image with contextualized deep net- works.TPAMI, 2019.

IRPO: Boosting Image Restoration via Post-training GRPO Joint rain detection and removal from a single image with contextualized deep net- works.TPAMI, 2019

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source=pdf_text observed=2026-08-03T19:24:39.368878Z digest=sha256:fc675868118973ca5d096201ed178e6d851ab23471b803df1e9fe179912d31ce

Observation 698a51c2-5987-4520-90f8-66e0c639e462 · outbound

This paper cites R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization.

IRPO: Boosting Image Restoration via Post-training GRPO R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization

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source=pdf_text observed=2026-08-03T19:24:39.371464Z digest=sha256:c753f4b3b4efe84edeccf2c3ca8044cb1003b01e1dc101a4f9878fe33a0206c8

Observation b49ba789-08b4-4342-a4ea-ed38daeffc4a · outbound

This paper cites Uncertainty guided multi-scale residual learning-using a cycle spinning cnn for single image de-raining.

IRPO: Boosting Image Restoration via Post-training GRPO Uncertainty guided multi-scale residual learning-using a cycle spinning cnn for single image de-raining

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source=pdf_text observed=2026-08-03T19:24:39.374648Z digest=sha256:40c95fa5df5384cc1a879cd02f53c9167797dde03efd1181cf283003c2b0f7d8

Observation 3eaf8ee9-696a-41b0-a272-a03eaa87085b · outbound

This paper cites Vla-r1: Enhancing rea- soning in vision-language-action models.arXiv preprint arXiv:2510.01623, 2025.

IRPO: Boosting Image Restoration via Post-training GRPO Vla-r1: Enhancing rea- soning in vision-language-action models.arXiv preprint arXiv:2510.01623, 2025

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source=pdf_text observed=2026-08-03T19:24:39.377324Z digest=sha256:336ab9bb00d1e4898d42d06a46201f0263ec725a817def7e417e4cf2162af933

Observation ebc83e44-7919-4f3f-b144-2ef19a304149 · outbound

This paper cites FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning.

IRPO: Boosting Image Restoration via Post-training GRPO FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning

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source=pdf_text observed=2026-08-03T19:24:39.380005Z digest=sha256:27d291ff2fe936f2a0fd94aa3252e24bbf8dae6cbad6750a3ff8ca98409e8680

Observation c4e3fc4f-c29c-4ae9-ab5d-152f430efd88 · outbound

This paper cites Multi-stage progressive image restoration.

IRPO: Boosting Image Restoration via Post-training GRPO Multi-stage progressive image restoration

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source=pdf_text observed=2026-08-03T19:24:39.382892Z digest=sha256:b02043f9957add86f5a32661dec48ced266e935da082ac2fa5c471333311ee7e

Observation 39d1a251-87a4-4915-82d7-016919c1676d · outbound

This paper cites Restormer: Efficient transformer for high-resolution image restoration.

IRPO: Boosting Image Restoration via Post-training GRPO Restormer: Efficient transformer for high-resolution image restoration

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source=pdf_text observed=2026-08-03T19:24:39.385656Z digest=sha256:5f3df6d060b6fc6f6895ae65bc1e1c5f5d81f9e7468c4f329d18edfebf8d23db

Observation f70f38bf-3022-469b-976e-38e539f516ff · outbound

This paper cites Learning enriched features for fast image restoration and enhancement.TPAMI, 2022.

IRPO: Boosting Image Restoration via Post-training GRPO Learning enriched features for fast image restoration and enhancement.TPAMI, 2022

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Observation ebb0888e-a5df-4799-896e-877de2bdf314 · outbound

This paper cites Density-aware single image de-raining using a multi-stream dense network.

IRPO: Boosting Image Restoration via Post-training GRPO Density-aware single image de-raining using a multi-stream dense network

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Observation cd26257a-c190-4665-846e-a822c7c7573f · outbound

This paper cites Ingredient-oriented multi- degradation learning for image restoration.

IRPO: Boosting Image Restoration via Post-training GRPO Ingredient-oriented multi- degradation learning for image restoration

Reference 76

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source=pdf_text observed=2026-08-03T19:24:39.394231Z digest=sha256:f89e164b702e9879d8b065e0b20400232163431038a95f3c0438bac9ea5a1bc8

Observation 5cdd2b03-e76b-418c-aa8c-b937ce460b06 · outbound

This paper cites R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization.

IRPO: Boosting Image Restoration via Post-training GRPO R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization

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source=pdf_text observed=2026-08-03T19:24:39.396917Z digest=sha256:80430bd638d645e993e98a34e22b65988180ff79c768f44ac3a76741b4acc100

Observation ba4e89bd-6b3a-4286-ba60-e370fa7c969b · outbound

This paper cites Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.TIP, 2017.

IRPO: Boosting Image Restoration via Post-training GRPO Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.TIP, 2017

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source=pdf_text observed=2026-08-03T19:24:39.399893Z digest=sha256:028ada67fab14a8bb9c821339ce5af57d1258f867e54b221c3437e27195b9c76

Observation 55ed92a6-aa8a-4ce4-86d4-fd8e6ebbbaa7 · outbound

This paper cites Learning deep CNN denoiser prior for image restoration.

IRPO: Boosting Image Restoration via Post-training GRPO Learning deep CNN denoiser prior for image restoration

Reference 79

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source=pdf_text observed=2026-08-03T19:24:39.402677Z digest=sha256:0fcee408c6bde50130687b3e0b5a157b9e03b3f1bb1824d90ade9159760e3e58

Observation 70c5ca71-622a-45c4-95f5-579ff748bad4 · outbound

This paper cites Ffdnet: Toward a fast and flexible solution for cnn-based image denoising.

IRPO: Boosting Image Restoration via Post-training GRPO Ffdnet: Toward a fast and flexible solution for cnn-based image denoising

Reference 80

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source=pdf_text observed=2026-08-03T19:24:39.405419Z digest=sha256:d98ea472e14c35250a594ee885879a33fbcd17f73926785f443d3a7fc8b040e9

Observation 9f3cd35b-f4e5-4146-b3d4-03efebe9ad37 · outbound

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

IRPO: Boosting Image Restoration via Post-training GRPO The unreasonable effectiveness of deep features as a perceptual metric

Reference 81

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source=pdf_text observed=2026-08-03T19:24:39.408449Z digest=sha256:81fc390a64b4e4346ad6575f6b0104875b7807025968836bc9c6467f44171e07

Observation 3e75ad3c-7547-46d3-a9ae-fc620b2cdff9 · outbound

This paper cites an unresolved cited work.

IRPO: Boosting Image Restoration via Post-training GRPO Unresolved cited work

Reference 82

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source=pdf_text observed=2026-08-03T19:24:39.411387Z digest=sha256:2d9c2a43b779d155c4c3d84e3026e97b9d7394d5b72bd0d412fb6692254d07a0

Observation ed38011c-d8a3-4483-8138-92e7123611f3 · outbound

This paper cites Real-world remote sensing image dehaz- ing: Benchmark and baseline.IEEE Transactions on Geo- science and Remote Sensing, 2025.

IRPO: Boosting Image Restoration via Post-training GRPO Real-world remote sensing image dehaz- ing: Benchmark and baseline.IEEE Transactions on Geo- science and Remote Sensing, 2025

Reference 83

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source=pdf_text observed=2026-08-03T19:24:39.414977Z digest=sha256:b6ca13e64a81b438c53796a1cb6d1f559792c7b8721574fac5d5ec3f6e8e3799

Observation b07e3c5a-b33f-4f0e-8711-80d04b2f9cf6 · outbound

This paper cites A robustly optimized bert pre-training approach with post-training.

IRPO: Boosting Image Restoration via Post-training GRPO A robustly optimized bert pre-training approach with post-training

Reference 84

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source=pdf_text observed=2026-08-03T19:24:39.417866Z digest=sha256:f6756a0090697d7b7f9a4b4e10ff97d635ae35fda4006fc47ede09e5679937ac

Observation 3c460d4c-b3ac-4c40-89cd-d3b3bb907776 · outbound

This paper cites an unresolved cited work.

IRPO: Boosting Image Restoration via Post-training GRPO Unresolved cited work

Reference 85

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source=pdf_text observed=2026-08-03T19:24:39.420904Z digest=sha256:dfe17544b58f039d743b6e33eb22e34aa8d12b06d45e1dc640fb5cd3af5fd75e

Observation 2480ac6b-b8a6-4a91-b599-500b4a0f7293 · outbound

This paper cites an unresolved cited work.

IRPO: Boosting Image Restoration via Post-training GRPO Unresolved cited work

Reference 86

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source=pdf_text observed=2026-08-03T19:24:39.423536Z digest=sha256:4ee76c290766ad54e59e02b8c5f6e99ac324162c42ff7e3548a340fa15244347

Observation 18e7216c-2258-4f9b-9a11-9d04196c0bfe · outbound

This paper cites an unresolved cited work.

IRPO: Boosting Image Restoration via Post-training GRPO Unresolved cited work

Reference 87

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source=pdf_text observed=2026-08-03T19:24:39.426299Z digest=sha256:3167d1395b5ba630045dd3ab10cb6e96a5db2bb3dc8ea5c22aef8cbd934d22f3

Observation 4c0c97c6-c61e-4085-861d-4d2fb99c099d · outbound

This paper cites Consider categories such as denoising (0/1/2, different noise levels), deraining (3), dehaz- ing (4), deblurring (5), or low-light enhancement (6).

IRPO: Boosting Image Restoration via Post-training GRPO Consider categories such as denoising (0/1/2, different noise levels), deraining (3), dehaz- ing (4), deblurring (5), or low-light enhancement (6)

Reference 88

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source=pdf_text observed=2026-08-03T19:24:39.428968Z digest=sha256:116f5551b2fff0233b0b7f1e847b5cddafe03369be390e539be60b203bec32ca

Observation 10bb0c0f-0014-4b90-a713-88c0c4e93167 · outbound

This paper cites Pay attention to: • Noise or streak removal quality for denois- ing/deraining.

IRPO: Boosting Image Restoration via Post-training GRPO Pay attention to: • Noise or streak removal quality for denois- ing/deraining

Reference 89

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source=pdf_text observed=2026-08-03T19:24:39.431602Z digest=sha256:035e4b6ae48b54747f3bae1bb41d92bb3821e9e68de7e2d2f1c776df0689fd4c

Observation 997ba2b7-131f-41d7-b6c7-08bf80a295ac · outbound

This paper cites an unresolved cited work.

IRPO: Boosting Image Restoration via Post-training GRPO Unresolved cited work

Reference 90

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source=pdf_text observed=2026-08-03T19:24:39.434201Z digest=sha256:6fa75488b49e94fefd4b0a34d81dfbb99c213491ade73b35c14c72d417c725a3

Observation b23c2086-8dcd-42ea-a965-738ecee70657 · outbound

This paper cites an unresolved cited work.

IRPO: Boosting Image Restoration via Post-training GRPO Unresolved cited work

Reference 91

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source=pdf_text observed=2026-08-03T19:24:39.437108Z digest=sha256:4ed0580d2b38baa8e53ccb538ac67113ae2629b8555a89fa70eb526e086e542b

Pith citing papers

Observation 8a52bf5e-2f8e-46dd-967e-9ed89c3bb46c · inbound

Bridging Information Asymmetry: A Hierarchical Framework for Deterministic Blind Face Restoration cites this paper.

Bridging Information Asymmetry: A Hierarchical Framework for Deterministic Blind Face Restoration IRPO: Boosting Image Restoration via Post-training GRPO

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source=pdf_text observed=2026-08-03T07:38:50.237882Z digest=sha256:2297b0393998e35b3581dcd33b030fab198b7b2e7df9b663e3db567a443526de