Pith. sign in

REVIEW 3 major objections 2 minor 58 references

One pocket to activate them all: Efforts on understanding the modulator pocket in K2P channels

T0 review · 3 major / 2 minor · reviewed 2026-08-15 · deepseek-v4-flash

Pith's one-line read The paper claims K2P channels share a common amphipathic modulator pocket whose sequence variations determine ligand selectivity, but the supplied full text is an unrelated low-light image enhancement paper.

desk verdict K2P abstract, unrelated low-light-image full text — the file is a mismatched shell, so there is no actual paper to evaluate. read the letter →

arxiv 2508.17891 v1 pith:7ETBA7ON submitted 2025-08-25 physics.bio-ph physics.comp-phq-bio.BM

classification physics.bio-phphysics.comp-phq-bio.BM
keywords K2PchannelsmodulatorpocketTREK1amphipathicchannelgatingligandselectivitypotassiummodulatorsabstractfull-textmismatch
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to establish that K2P potassium channels, the family that includes TREK1, share a common modulator pocket: a cryptic, largely amphipathic site where agonists bind to increase channel activity. The abstract argues that sequence variations within this pocket determine ligand selectivity and that the activation signal from the pocket is transduced to the channel gates. If the claim is right, the pocket becomes a single architectural target for designing selective K2P channel modulators. However, the supplied full text is a different paper entirely, describing a transformer for low-light image enhancement, with no K2P content. The abstract's claim is therefore stated but unsupported in this submission.

What carries the argument

The central object is the modulator pocket itself: a cryptic, largely amphipathic binding cavity at the membrane-water interface, first found in TREK1 and proposed to recur across K2P channels. The argument's load is carried by the pocket's amphipathic character and its sequence variations, which are what let the abstract explain both shared agonist binding and subtype-selective pharmacology. These features also supply the structural target for designing new modulators. In the submitted document, this machinery is described only in the abstract; the full text does not develop or test it.

What would settle it

Open the submitted full text and search for any mention of K2P, TREK1, or modulator pocket: there is none, so the abstract's central claim has no supporting analysis in this document. If the claim itself were tested, it would be falsified by a K2P channel structure whose proposed modulator pocket is not amphipathic, or by mutations at the pocket that leave agonist activation unchanged.

Watch

Extended reading notes

Core claim

The central discovery claimed in the abstract is that the modulator pocket, a cryptic site first identified in the TREK1 K2P channel, is a common architectural feature across K2P channels. It is described as largely amphipathic because it sits at the interface between the hydrophobic membrane and the aqueous solvent, and it carries channel-specific sequence variations that explain differential ligand binding. The abstract further claims that agonists bound at this pocket generate an activation signal transduced to the channel gates, and that this architecture can guide the design of selective, potent modulators. In the supplied manuscript, this discovery appears only in the abstract; the body text is an unrelated low-light image enhancement paper, so the claimed evidence is not present.

Load-bearing premise

The entire K2P argument rests on the submitted manuscript actually containing the K2P review promised in the abstract; in this submission that premise is false, because the body is an unrelated low-light image enhancement paper.

Editorial extensions

If this is right

  • If the common-pocket claim holds, a single structural template could guide the design of K2P activators across the channel family.
  • Sequence variations in the pocket would become the natural predictor of subtype-selective ligand behavior, enabling targeted pharmacology.
  • Mutations at the pocket would be expected to alter agonist-dependent gating in predictable ways, making them testable functional variants.
  • The amphipathic nature of the pocket implies that effective ligands need both hydrophobic and polar features, which could shape medicinal chemistry campaigns.
  • Structure-based screening could target the membrane-water interface rather than the canonical central pore of the channel.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The supplied full text is an unrelated ISALux low-light image enhancement paper, so any claim about K2P gating in this document is unsupported by the body text.
  • If the common-pocket hypothesis is correct, the resolved TREK1 structure could serve as a template to homology-model the pocket in less-studied K2P channels and to predict off-target binding.
  • The amphipathic-pocket hypothesis implies that the choice of membrane mimetic or detergent in structural studies could distort pocket shape, a possibility that could be tested by comparing structures in different environments.
  • A direct experimental test would be to mutate polar versus hydrophobic residues at the proposed pocket boundary and measure whether agonist efficacy shifts as the amphipathic balance predicts.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The submission, identified by its abstract as a review of the modulator pocket in two-pore-domain potassium (K2P) channels, promises to synthesize existing work on gating mechanisms, agonist binding at the modulator pocket, mutations affecting gating, and transduction of activation signals, and to propose a common amphipathic modulator pocket architecture. The full text supplied, however, is not this review. It is the manuscript 'ISALux: Illumination and Semantics-Aware Transformer Employing Mixture of Experts for Low-Light Image Enhancement' (arXiv:2508.17885v1 [cs.CV]), with different title, authors, abstract, and content. The document contains no mention of TREK1, K2P channels, modulator pockets, gating, agonists, or mutations. Consequently, none of the claims in the abstract can be checked against any supporting body text, figures, equations, or references.

Significance. The review described in the abstract would be of interest to the ion-channel pharmacology community: a synthesized account of a cryptic allosteric site across K2P channels, with an explicit structural proposal and implications for selective modulator design, could be a useful contribution. No aspect of that contribution is present in the submitted manuscript. There are no derivations, no data tables, no structural analyses, and no references on K2P channels to evaluate; the one falsifiable proposal (the common amphipathic pocket architecture with sequence variations) is stated only in the abstract. As submitted, the manuscript cannot be assessed on its scientific merits.

major comments (3)
  1. [Abstract vs. full text] The full text of the submitted manuscript is the ISALux low-light image enhancement paper (arXiv:2508.17885v1 [cs.CV]), not the K2P modulator-pocket review promised by the abstract. There is no content in Sections 1–6 about TREK1, K2P channels, the modulator pocket, gating mechanisms, agonists, or mutations; therefore the abstract's central claim of a common amphipathic modulator pocket architecture has zero evidential support in the submitted document.
  2. [Full text, Sections 1–6] The review structure promised in the abstract—(i) description of gating mechanisms, (ii) experimental and computational evidence for modulator-pocket agonists, (iii) mutations at the site that affect gating, and (iv) transduction of the activation signal to the channel gates—is entirely absent. Instead, Sections 1–6 present a transformer architecture for image enhancement, with Equations (1)–(14) and Tables 1–3 reporting PSNR, SSIM, and NIQE results. None of the promised review sections exists, so the scientific argument cannot be evaluated.
  3. [Manuscript header] The manuscript header on the first page prints arXiv:2508.17885v1 [cs.CV], a different identifier and category from the submission's arXiv:2508.17891 (physics.bio-ph), and the title and author list differ from those of the submitted abstract. This document-level inconsistency prevents any assessment of the K2P review's content; it is a load-bearing issue, not a typographical nit.
minor comments (2)
  1. [Title/abstract metadata] The title 'One pocket to activate them all' and the abstract describe a K2P channel review, while the body carries the title 'ISALux: Illumination and Semantics-Aware Transformer Employing Mixture of Experts for Low-Light Image Enhancement'; the metadata and body should be reconciled.
  2. [References] The reference list in the full text is entirely about low-light image enhancement and computer vision; not a single citation pertains to K2P channels or the modulator pocket.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity can be established: the supplied full text is a different paper, so the K2P-review claim has no derivational chain to audit.

full rationale

The abstract of arXiv:2508.17891 promises a review of the modulator pocket in K2P channels and 'outline[s] a potential common modulator pocket architecture across K2P channels.' The supplied body text, however, is ISALux, an unrelated low-light image enhancement paper (arXiv:2508.17885v1 [cs.CV]) by different authors. There is no K2P content in the body: no TREK1, no modulator pocket, no gating mechanisms, no agonist data, no mutation analysis, and no signal-transduction argument. Under the review rules, this missing support must be flagged: the abstract's central claim is unverifiable from the supplied document. But absent any body derivation, there is no equation, fitted parameter, or self-citation chain that reduces the claimed architecture to its inputs by construction. A review synthesis of prior literature is not itself a circular derivation, and the document mismatch is a completeness problem rather than a circularity problem. Since circularity can only be claimed when a specific reduction is exhibited, and no such reduction exists here, the honest finding is no significant circularity (score 0).

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

No free parameters can be identified from the abstract because no equations or fitted values are given. The axioms listed are the domain-level premises the abstract relies on. The supplied body, being an unrelated ML paper, provides no support for any of them.

assumptions (2)
  • domain assumption Published K2P structures, ligand-binding studies, and mutations are sufficient to define a conserved modulator pocket across K2P channels.
    The abstract's proposed common architecture is presented as a synthesis of prior reports; the supplied full text contains no independent structural or functional validation to test this premise.
  • domain assumption Amphipathic character at the membrane-water interface can be used as an organizing principle for K2P activation.
    The abstract asserts this consistency as supporting evidence; without the review body, the argument from amphipathicity cannot be checked.

how reviews work

0 comments
Cite this review

Pith. "Pith review of One pocket to activate them all: Efforts on understanding the modulator pocket in K2P channels." pith.science (2026). https://pith.science/paper/7ETBA7ON

@misc{pith2026250817891,
  author       = {Pith},
  title        = {Pith review of: One pocket to activate them all: Efforts on understanding the modulator pocket in K2P channels},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7ETBA7ON}},
  note         = {Machine review of arXiv:2508.17891}
}
read the original abstract

The modulator pocket is a cryptic site discovered in the TREK1 K2P channel that accommodates agonists capable of increasing the channel's activity. Since its discovery, equivalent sites in other K2P channels have been shown to bind various ligands, both endogenous and exogenous. In this review, we attempt to elucidate how the modulator pocket contributes to K2P channel activation. To this end, we first describe the gating mechanisms reported in the literature and rationalize their modes of action. We then highlight previous experimental and computational evidence for agonists that bind to the modulator pocket, together with mutations at this site that affect gating. Finally, we elaborate how the activation signal arising from the modulator pocket is transduced to the gates in K2P channels. In doing so, we outline a potential common modulator pocket architecture across K2P channels: a largely amphipathic structure -consistent with the expected properties of a pocket exposed at the interface between a hydrophobic membrane and the aqueous solvent- but still with some important channel-sequence-variations. This architecture and its key differences can be leveraged for the design of new selective and potent modulators.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

58 extracted references · 52 canonical work pages

  1. [1]

    W. Wang, X. Wu, X. Yuan, and Z. Gao. An experiment-based review of low-light image enhancement methods. IEEE Access, 8:87884–87917, 2020

  2. [2]

    Low- light image and video enhancement using deep learning: A survey

    Chongyi Li, Chunle Guo, Linghao Han, Jun Jiang, Ming-Ming Cheng, Jinwei Gu, and Chen Change Loy. Low- light image and video enhancement using deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 8

  3. [3]

    R. Wang, Q. Zhang, C.-W. Fu, X. Shen, W.-S. Zheng, and J. Jia. Underexposed photo enhancement using deep illumination estimation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019

  4. [4]

    Moran, P

    S. Moran, P. Marza, S. McDonagh, S. Parisot, and G. Slabaugh. DeepLPF: Deep local parametric filters for image enhancement. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020

  5. [5]

    Z. Wang, X. Cun, J. Bao, W. Zhou, J. Liu, and H. Li. Uformer: A general u-shaped transformer for image restoration. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022

  6. [6]

    Deep retinex decomposition for low-light enhance- ment

    Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retinex decomposition for low-light enhance- ment. In Proceedings of the British Machine Vision Conference (BMVC), 2018

  7. [7]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Proceedings of NeurIPS, 2017

  8. [8]

    Lora: Low-rank adaptation of large language models

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3, 2022

Show all 58 references
  1. [9]

    Kindling the darkness: A practical low-light image enhancer

    Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In ACM International Conference on Multimedia, 2019

  2. [10]

    Zhang, Y

    Y . Zhang, Y . Tian, Y . Kong, B. Zhong, and Y . Fu. Residual dense network for image restoration. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020

  3. [11]

    X. Yi, H. Xu, Hao Zhang, Linfeng Tang, and Jiayi Ma. Diff-retinex: Rethinking low-light image enhance- ment with a generative diffusion model. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

  4. [12]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, and Sylvain Gelly. An image is worth 16x16 words: Transformers for image recognition at scale. International Conferenc...

  5. [13]

    Tokens-to-token vit: Training vision transformers from scratch on imagenet

    Li Yuan, Yunpeng Chen, TaoWang, Weihao Yu, Yujun Shi, Zihang Jiang, Francis EH Tay, Jiashi Feng, , and Shuicheng Yan. Tokens-to-token vit: Training vision transformers from scratch on imagenet. IEEE/CVF Inter- national Conference on Computer Vision (ICCV), 2021

  6. [14]

    Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

    Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. IEEE/CVF International Conference on Computer Vision (ICCV), 2021

  7. [15]

    Zheng, J

    S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y . Wang, Y . Fu, J. Feng, T. Xiang, and P. HS Torr. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021

  8. [16]

    Hierarchical vision transformer using shifted windows

    Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. Hierarchical vision transformer using shifted windows. European Conference on Computer Vision (ECCV), 2020

  9. [17]

    Z. Liu, Y . Linand, Y . Cao, H. Hu, Y . Wei, Z. Zhang, S. Lin, and B. Guo. Swin transformer: Hierarchical vision transformer using shifted windows. IEEE/CVF International Conference on Computer Vision (ICCV), 2021

  10. [18]

    Learning texture transformer network for image super-resolution

    Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, and Baining Guo. Learning texture transformer network for image super-resolution. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020

  11. [19]

    Activating more pixels in image super-resolution transformer

    Xiangyu Chen, Xintao Wang, Jiantao Zhou, and Chao Dong. Activating more pixels in image super-resolution transformer. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

  12. [20]

    Vision transformers for single image dehazing

    Yuda Song, Zhuqing He, Hui Qian, and Xin Du. Vision transformers for single image dehazing. IEEE Transac- tions on Image Processing, 2023

  13. [21]

    Guibas, Dilip Krishnan, Kilian Q Weinberger, Yonglong Tian, and Yue Wang

    Jiawei Yang, Katie Z Luo, Jiefeng Li, Congyue Deng, Leonidas J. Guibas, Dilip Krishnan, Kilian Q Weinberger, Yonglong Tian, and Yue Wang. Dvt: Denoising vision transformers. 2024

  14. [22]

    X. Xu, R. Wang, C.-W. Fu, and J. Jia. SNR-aware low-light image enhancement. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022

  15. [23]

    Zhang, Y

    Z. Zhang, Y . Jiang, J. Jiang, X. Wang, P. Luo, and J. Gu. STAR: A structure-aware lightweight transformer for real-time image enhancement. IEEE/CVF International Conference on Computer Vision (ICCV), 2021

  16. [24]

    Z. Cui, K. Li, L. Gu, S. Su, P. Gao, Z. Jiang, Y . Qiao, and T. Harada. You only need 90k parameters to adapt light: a light weight transformer for image enhancement and exposure correction. In BMVC, 2022. 9

  17. [25]

    Retinexformer: One- stage retinex-based transformer for low-light image enhancement

    Yuanhao Cai, Hao Bian, Jing Lin, Haoqian Wang, Radu Timofte, and Yulun Zhang. Retinexformer: One- stage retinex-based transformer for low-light image enhancement. In IEEE/CVF International Conference on Computer Vision (ICCV), 2023

  18. [26]

    Glam: Efficient scaling of language models with mixture-of-experts

    Nan Du et al. Glam: Efficient scaling of language models with mixture-of-experts. International Conference on Machine Learning (ICML), 2022

  19. [27]

    Raphael: Text-to- image generation via large mixture of diffusion paths

    Zeyue Xue, Guanglu Song, Qiushan Guo, Boxiao Liu, Zhuofan Zong, Yu Liu, and Ping Luo. Raphael: Text-to- image generation via large mixture of diffusion paths. Proceedings of NeurIPS, 2023

  20. [28]

    Cumo: Scaling multimodal llm with co-upcycled mixture-of-experts

    Jiachen Li, Xinyao Wang, Sijie Zhu, Chia-wen Kuo, Lu Xu, Fan Chen, Jitesh Jain, Humphrey Shi, and Longyin Wen. Cumo: Scaling multimodal llm with co-upcycled mixture-of-experts. arXiv:, 2024

  21. [29]

    Self-moe: Towards compositional large language models with self- specialized experts, 2024

    Junmo Kang, Leonid Karlinsky, Hongyin Luo, Zhen Wang, Jacob Hansen, James Glass, David Cox, Rameswar Panda, Rogerio Feris, and Alan Ritter. Self-moe: Towards compositional large language models with self- specialized experts, 2024

  22. [30]

    Towards understanding the mixture-of- experts layer in deep learning

    Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, and Yuanzhi Li. Towards understanding the mixture-of- experts layer in deep learning. Proceedings of NeurIPS, 2022

  23. [31]

    Patch-level routing in mixture-of-experts is provably sample-efficient for convolutional neural networks

    Mohammed Nowaz Rabbani Chowdhury, Shuai Zhang, Meng Wang, Sijia Liu, and Pin-Yu Chen. Patch-level routing in mixture-of-experts is provably sample-efficient for convolutional neural networks. International Con- ference on Machine Learning (ICML), 2023

  24. [32]

    Adamv-moe: Adap- tive multi-task vision mixture-of-experts

    Tianlong Chen, Xuxi Chen, Xianzhi Du, Abdullah Rashwan, Fan Yang, and Huizhong Chen. Adamv-moe: Adap- tive multi-task vision mixture-of-experts. In IEEE/CVF International Conference on Computer Vision (ICCV) , 2023

  25. [33]

    Ace: Ally complementary experts for solving long-tailed recognition in one-shot

    Jiarui Cai, Yizhou Wang, and Jenq-Neng Hwang. Ace: Ally complementary experts for solving long-tailed recognition in one-shot. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021

  26. [34]

    Blind single image super-resolution with a mixture of deep networks

    Yifan Wang, Lijun Wang, Hongyu Wang, Peihua Li, and Huchuan Lu. Blind single image super-resolution with a mixture of deep networks. Pattern Recognition, 2020

  27. [35]

    Moesr: Blind super-resolution using kernel-aware mixture of experts

    Mohammad Emad, Maurice Peemen, and Henk Corporaal. Moesr: Blind super-resolution using kernel-aware mixture of experts. IEEE Workshop on Applications of Computer Vision (WACV), 2022

  28. [36]

    Parameter efficient adaptation for image restoration with heterogeneous mixture-of-experts

    Hang Guo, Tao Dai, Yuanchao Bai, Bin Chen, Xudong Ren, Zexuan Zhu, and Shu-Tao Xia. Parameter efficient adaptation for image restoration with heterogeneous mixture-of-experts. Proceedings of NeurIPS, 2024

  29. [37]

    Rethinking atrous convolution for semantic image segmentation

    Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017

  30. [38]

    Microsoft coco: Common objects in context

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer vision–ECCV 2014: 13th Euro- pean conference, zurich, Switzerland, September 6-12, 2014, proceed...

  31. [39]

    Jacobs, Michael I

    Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, and Geoffrey E. Hinton. Adaptive mixtures of local experts. Neural Computation, 3(1):79–87, 1991

  32. [40]

    Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

    Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538, 2017

  33. [41]

    S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang. Restormer: Efficient transformer for high-resolution image restoration. In IEEE/CVF Conf. on Computer Vision and Pattern Recog. (CVPR), 2022

  34. [42]

    Y . Wang, R. Wan, W. Yang, H. Li, Lap-Pui Chau, and A. Kot. Low-light image enhancement with normalizing flow. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 2604–2612, 2022

  35. [43]

    T. Wang, K. Zhang, T. Shen, W. Luo, B. Stenger, and T. Lu. Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method. In AAAI Conference on Artificial Intelligence, 2023

  36. [44]

    H. Zhou, W. Dong, X. Liu, S. Liu, X. Min, G. Zhai, and J. Chen. Glare: Low light image enhancement via generative latent feature based codebook retrieval. In European Conference on Computer Vision (ECCV), 2024

  37. [45]

    Very deep convolutional networks for large-scale image recognition

    Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations (ICLR), 2014

  38. [46]

    Sparse gradient regularized deep retinex network for robust low-light image enhancement

    Wenhan Yang, Wenjing Wang, Haofeng Huang, Shiqi Wang, and Jiaying Liu. Sparse gradient regularized deep retinex network for robust low-light image enhancement. IEEE Transactions on Image Processing , 30:2072– 2086, 2021. 10

  39. [47]

    R. Wang, X. Xu, C.-W. Fu, J. Lu, B. Yu, and J. Jia. Seeing dynamic scene in the dark: A high-quality video dataset with mechatronic alignment. In IEEE/CVF International Conference on Computer Vision (ICCV), 2021

  40. [48]

    S. Zhou, C. Li, and C. C. Loy. Lednet: joint low-light enhancement and deblurring in the dark. Lecture Notes in Computer Science, pages 573–589, 2022

  41. [49]

    Lime: Low-light image enhancement via illumination map estimation

    Xiaojie Guo, Yu Li, and Haibin Ling. Lime: Low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, 26(2):982–993, 2016

  42. [50]

    Naturalness preserved enhancement algorithm for non- uniform illumination images

    Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non- uniform illumination images. IEEE Transactions on Image Processing, 22(9):3538–3548, 2013

  43. [51]

    Perceptual quality assessment for multi-exposure image fusion

    Kede Ma, Kai Zeng, and Zhou Wang. Perceptual quality assessment for multi-exposure image fusion. IEEE Transactions on Image Processing, 24(11):3345–3356, 2015

  44. [52]

    Contrast enhancement based on layered difference representation of 2d histograms

    Chulwoo Lee, Chul Lee, and Chang-Su Kim. Contrast enhancement based on layered difference representation of 2d histograms. IEEE Transactions on Image Processing, 22(12):5372–5384, 2013

  45. [53]

    completely blind

    Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters, 20(3):209–212, 2012

  46. [54]

    Deblurgan-v2: Deblurring (orders-of- magnitude) faster and better

    Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang. Deblurgan-v2: Deblurring (orders-of- magnitude) faster and better. In The IEEE International Conference on Computer Vision (ICCV), Oct 2019

  47. [55]

    Learning semantic-aware knowledge guidance for low-light image enhancement

    Wu Yuhui, Pan Chen, Wang Guoqing, Yang Yang, Wei Jiwei, Li Chongyi, and Heng Tao Shen. Learning semantic-aware knowledge guidance for low-light image enhancement. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

  48. [56]

    You do not need additional priors or regularizers in retinex-based low-light image enhancement

    Huiyuan Fu, Wenkai Zheng, Xiangyu Meng, Xin Wang, Chuanming Wang, and Huadong Ma. You do not need additional priors or regularizers in retinex-based low-light image enhancement. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 18125–18134, 2023

  49. [57]

    Asp-led: Learning ambiguity-aware structural priors for joint low-light enhancement and deblurring

    Jing Ye, Yang Liu, Congjing Yu, Changzhen Qiu, and Zhiyong Zhang. Asp-led: Learning ambiguity-aware structural priors for joint low-light enhancement and deblurring. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 12389–12396, 2024

  50. [58]

    Vqcnir: Clearer night image restoration with vector-quantized codebook

    Wenbin Zou, Hongxia Gao, Tian Ye, Liang Chen, Weipeng Yang, Shasha Huang, Hongsheng Chen, and Sixiang Chen. Vqcnir: Clearer night image restoration with vector-quantized codebook. arXiv preprint arXiv:2312.08606, 2023. 11

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.