Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The paper presents a Retinex-based low light enhancement approach that explicitly identifies and removes noise as part of the process. The algorithm uses an iterative Retinex-based method for computing both the illumination and a noise map simultaneously. The computed illumination map is designed to minimize noise, leaving the noise signal in the reflectance map (reflectance = image / illumination). The computed noise map and the computed reflectance map are the inputs to a U-net trained to produce a clean reflectance map. The computed illumination map is passed through a neural network to enhance the lighting.  Multiplying the clean reflectance map with the enhanced illumination map updated produces the clean, re-lit output. The method is evaluated against multiple other methods on a standard data set.
strength_and_weaknesses: The method demonstrates good results both qualitatively in the selected examples and quantitatively on the LOL data set. The explicit computation of the noise map and forcing the noise signal into the reflectance are interesting contributions to this problem. The separation of the illumination and reflectance for noise removal helps to mask heavy-handed processing in the reflectance.

The paper could use a second pass with an editor. For example, in the last sentence of section 3.2, the phrase "The illumination is processed to" should be removed from the sentence.

The description of the unfolding total variation model, section 3.1, has some undefined elements. For example, the symbol rho sub-r in equation 4 is not defined in the text. Instead, a variable r is referenced, which seems to be referencing rho. 

The authors note that a gamma correction is often used for the illumination modification process, but they chose to use a six layer NN. This modification is not evaluated in the ablation study, but it would be interesting to understand the importance of this substitution to the overall performance, especially given the difference in computational complexity.

How different is the noise map from a simple map of average intensity?  If the noise is correlated with overall intensity, then the average local intensity might be a sufficient approximation to the noise levels. The paper does not really discuss qualities or properties of the identified noise maps and whether a simpler approach might work as well.
clarity,_quality,_novelty_and_reproducibility: The approach to reducing noise by using the Retinex illumination estimation process is a novel contribution to the problem of low light enhancement. The work seems to be reproducible, with the caveat that the total variation process is not fully described in this paper, but is available in the referenced work.

As noted in several places, this is derivative work from a prior paper.
summary_of_the_review: The approach makes good use of the illumination/reflectance separation by driving the noise into the reflectance map and then repairing the reflectance map. The end-to-end training of both the noise reduction and the illumination enhancement enable them to be complementary in solving the problem.

The method presentation could be improved, and there are some additional items it would be nice to see in the ablation study.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: This paper proposes a Retinex-based low-light image enhancement model. The model first obtains an illumination map and noise level maps by applying an unfolding total variation network in (Zheng et al., 2021). Then, it obtains a noisy reflectance map obtained by using the illumination map and denoise the reflectance map guided by the noise level maps from the previous step. Finally, it obtains the final enhanced image by combining the denoised reflectance map and a relit illumination map.
strength_and_weaknesses: * Strength:

Although it is a rather straightforward adaptation of an existing work, UTV (Zheng et al., 2021), the resulting model performs pretty well. On VE-LOL evaluation in Table 2, theirs even outperforms a SoTA model, LLFlow (Wang et al., 2022). 

* Weakness:

The proposed model is a rather simple adaptation of UTV (Zheng et al., 2021), where the only major difference seems to be replacing the subtraction/addition to extract/recombine a detail layer with a division/multiplication to extract/recombine a reflectance layer based on a Retinex theory.

They did not compare with the most similar work, UTV (Zheng et al., 2021), from which they borrowed most mechanisms. Also, for some reason, they did not evaluate on sRGB-SID and ELD datasets, where UTV (Zheng et al., 2021) was tested on.

LLFlow (Wang et al., 2022) is not discussed in the text and also missing in the references.
clarity,_quality,_novelty_and_reproducibility: The manuscript is overall incomplete. It affects badly on the clarity of the work. It is often hard to fully follow what is being described.

The novelty is not very high, because their algorithm is very similar to UTV (Zheng et al., 2021) except for employing the Retinex theory.

They did not even attempt to make a clear distinction from the baseline work, UTV (Zheng et al., 2021), in the paper. They did not compare with it nor evaluate on the same datasets.
summary_of_the_review: The idea is rather straightforward but quite effective to produce nice results. However, the paper is largely incomplete and evaluation is insufficient. In order to prove its potential as an independent work, a clear distinction from the baseline work UTV (Zheng et al., 2021) needs to be made along with sufficient evaluation and discussions.
review_title: null

final_score: rating: 1: strong reject
review_content: summary_of_the_paper: The authors propose a low-light image enhancement method RetinexUTV. The proposed method can estimate a noise level map and a illumination map by an unfolded total variational network and generate a noise-free reflection map guided by the learned noise level map. Finally, the output can be obtain by multiplying the noise-free reflection map and the illumination map.
strength_and_weaknesses: The motivation of this article is significant, while the solution is too simple and simply splices the ideas of other papers. Moreover, there are many problems in the paper, such as syntax errors, lack of references, etc.
clarity,_quality,_novelty_and_reproducibility: 1) The author disclosed their information in Section 5--‘properties(Li et al., 2021). Our previous work, RetinexDIP(Zhao et al., 2021), is an unsupervised method that can enhance various low-light conditions’.  It violates the double-blind rule for manuscript review.

2) This paper is hard to follow and has many typos. The author should revise them carefully
(1) On page 3: the variables I (I) and R (R) are written irregularly; 
(2) On page 3: iterations, Therefore,
(3) On page 3: For example, use BM3D
(4) On page 3: and then The
(5) On page 4: component. , namely S = I · (R + N). 
…
3) This article lacks innovation. It simply splices the ideas of other papers. For example, the unfolding total variation network borrows from Zheng et al.[1].

4) Incomplete reference. For example, the author compares the proposed method with LLFlow (Wang et al. 2022)[2] and MIRNet (Zamir et al. 2020)[3] in Tabel 1 and Tabel 2, but we can't find LLFlow (Wang et al. 2022)[2] and MIRNet (Zamir et al. 2020)[3] in the references.

[1] Chuanjun Zheng, Daming Shi, and Wentian Shi. Adaptive unfolding total variation network for low-light image enhancement. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4439–4448, 2021.
[2]Yufei Wang, Renjie Wan, Wenhan Yang, Haoliang Li, Lap-Pui Chau, Alex C. Kot. Low-Light Image Enhancement with Normalizing Flow. In Proceedings of the AAAI Conference on Artificial Intelligence. 2022
[3] Syed Waqas Zamir, Aditya Arora, Salman H. Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao. Learning Enriched Features for Real Image Restoration and Enhancement. In Proceedings of the European Conference on
Computer Vision, 2020.
5) The experiment was inadequate. The authors did not conduct ablation experiments to prove their motivation.
summary_of_the_review: The motivation of this article is significant, while the solution is too simple and simply splices the ideas of other papers. Moreover, there are many problems in the paper, such as syntax errors, lack of references, etc. Therefore, this paper does not meet the acceptance requirements of ICLR.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: The paper proposes a low-light enhancement method to simultaneously enhance low-light images and suppress noise. The proposed method obtains the noise level of a low-light image and initial illumination layer using a network. Based on the initial illumination layer and the noise level, another network is used to estimate the initial reflectance layer where the noise is suppressed. Finally the initial illumination layer is adjusted using another network, and the output is multiplied with the reflectance layer to obtain the enhanced image.
strength_and_weaknesses: Strengths:
1. The paper is easy to read, and the proposed pipeline is easy to understand and implement.

Weaknesses:
1. The paper is lack of novelty. There are no interesting and innovative ideas in the pipeline. The is no verification on whether the noise maps are accurate, particularly for noise that is beyond the training data. In real images taken from different cameras, different scenes, lighting, etc, noise distributions and levels vary significantly. The images in the training data might not be able to capture all the variations. This issue is not only for noise, but also for illumination and reflectance layers, which in many cases do not have ground-truths.

2. The proposed unfolded total variational network seems to be similar to Zheng et al.'s adaptive unfolding total variation network. 

3. Retinex-based low light enhancement has been explored in many papers (RetinexDIP, RetinexNet, LR3M, etc). The novelty of the paper is mainly on the noise maps, which the significance is unclear. Moreover, some recent papers are not discussed or compared with:
(1) Yang, Wenhan, et al. "Sparse gradient regularized deep retinex network for robust low-light image enhancement." IEEE Transactions on Image Processing 30 (2021): 2072-2086.
(2) Ren, Xutong, et al. "LR3M: Robust low-light enhancement via low-rank regularized retinex model." IEEE Transactions on Image Processing 29 (2020): 5862-5876.
(3) Wang, Yufei, et al. "Low-light image enhancement with normalizing flow." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. No. 3. 2022.
(4) Sharma and Tan, "Nighttime Visibility Enhancement by Increasing the Dynamic Range and Suppression of Light Effects", Computer Vision and Pattern Recognition, CVPR 2021.

4. For the qualitative evaluation, it is unclear how much the noise affecting the input images. The paper should boost the low-light images to show the level of noise. Different cameras produce different noise levels. A good evaluation shows that even with the presence of a high level of noise, the method works adequately. 


clarity,_quality,_novelty_and_reproducibility: The novelty is limited. Retinex-based algorithms have been introduced extensively in the literature. The unfolded total variational network seems to be taken from a published paper. Moreover, the experimental results are not convincing, as we do not know whether the proposed method can work on extremely noisy low-light images.
summary_of_the_review: Please see my comments on the novelty and experimental results above.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: The paper proposed a Retinex-based low-light image enhancement model, where an illumination map and noise level maps are obtained by applying an unfolding total variation network (UTV). A noisy reflectance map is obtained using the illumination map and is further denoised guided by the noise level maps. The final enhanced image was produced by combining the dense reflectance map and a relit illumination map.

Overall, the reviewers were negative about the paper, especially the novelty. As R-A79g and R-UJzM pointed out, the proposed system is very similar to UTV (Zheng et al., 2021) except for employing the Retinex theory, but the paper didn’t compare with UTV, and didn’t evaluate on sRGB-SID and ELD datasets where UTV was tested on. R-UJzM pointed out that the authors indeed disclosed their identity in Section 5, saying “Our previous work, ReinexDIP (Zhao et al., 2021)”. This is a violation of ICLR double-blind review policy. R-dZQX listed many relevant references that should have been included to understand the contribution of the paper as Retinex-based low light enhancement has been explored intensively in the community.

The authors did not provide rebuttal.

The AC agrees with the reviewers that the paper has significant flaws (including violating the double-blind review policy), and the contribution is not sufficient for publication.
justification_for_why_not_higher_score: The paper has obvious flaws: it revealed the author identity in Sect 5, and the contribution is insufficient due to missing comparisons and references.
Decision: Reject