Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration
In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels in one model. The proposed control problem contains a restoration dynamics which is modeled by an RNN. The moving endpoint, which is essentially the terminal time of the associated dynamics, is determined by a policy network. We call the proposed model the dynamically unfolding recurrent restorer (DURR). Numerical experiments show that DURR is able to achieve state-of-the-art performances on blind image denoising and JPEG image deblocking. Furthermore, DURR can well generalize to images with higher degradation levels that are not included in the training stage.
 Summary This paper decomposes the image restoration task in two part: the restoration part handled by a restoration RNN, and the number of steps to apply the RNN is determined using a policy unit. State of the art results are achieved on blind grey level Gaussian noise denoising on the BSD68 dataset. The approach is novel to my knowledge, the paper is well written, the results are good and well illustrated. Questions: -It would be nice to present results on color images, and on datasets that contains natural noises. -Lowering the learning rate on plateaus during training is done by hand or is there an automatic criterion to define the plateaus? Minor: page 1: extra ")" after ref to Bredies et al 2010 could cite Chen, Zu, Koltun ICCV17 in deep models for restoration Several "L" have been replaced by "_* e.g. under review at IC_R, R_-based, etc in the whole paper p.4: rain-> train greatly influence -> greatly influences p5: typo performace make a uniform bib: whole first name or abbr. , no URL, etc. p6: the weight -> the set of weights add the specification that the noise is Gaussian the sentence "the training set and testing set of ..." is used twice, remove one. p7 Table 1: the perf of DnCNN-B is 29.16 and not 29.15 for sigma 25, right?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration
In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels in one model. The proposed control problem contains a restoration dynamics which is modeled by an RNN. The moving endpoint, which is essentially the terminal time of the associated dynamics, is determined by a policy network. We call the proposed model the dynamically unfolding recurrent restorer (DURR). Numerical experiments show that DURR is able to achieve state-of-the-art performances on blind image denoising and JPEG image deblocking. Furthermore, DURR can well generalize to images with higher degradation levels that are not included in the training stage.
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<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 Summary This paper decomposes the image restoration task in two part: the restoration part handled by a restoration RNN, and the number of steps to apply the RNN is determined using a policy unit. State of the art results are achieved on blind grey level Gaussian noise denoising on the BSD68 dataset. The approach is novel to my knowledge, the paper is well written, the results are good and well illustrated. Questions: -It would be nice to present results on color images, and on datasets that contains natural noises. -Lowering the learning rate on plateaus during training is done by hand or is there an automatic criterion to define the plateaus? Minor: page 1: extra ")" after ref to Bredies et al 2010 could cite Chen, Zu, Koltun ICCV17 in deep models for restoration Several "L" have been replaced by "_* e.g. under review at IC_R, R_-based, etc in the whole paper p.4: rain-> train greatly influence -> greatly influences p5: typo performace make a uniform bib: whole first name or abbr. , no URL, etc. p6: the weight -> the set of weights add the specification that the noise is Gaussian the sentence "the training set and testing set of ..." is used twice, remove one. p7 Table 1: the perf of DnCNN-B is 29.16 and not 29.15 for sigma 25, right?
assistant
 Summary This paper decomposes the image restoration task in two part: the restoration part handled by a restoration RNN, and the number of steps to apply the RNN is determined using a policy unit. State of the art results are achieved on blind grey level Gaussian noise denoising on the BSD68 dataset. The approach is novel to my knowledge, the paper is well written, the results are good and well illustrated. Questions: -It would be nice to present results on color images, and on datasets that contains natural noises. -Lowering the learning rate on plateaus during training is done by hand or is there an automatic criterion to define the plateaus? Minor: page 1: extra ")" after ref to Bredies et al 2010 could cite Chen, Zu, Koltun ICCV17 in deep models for restoration Several "L" have been replaced by "_* e.g. under review at IC_R, R_-based, etc in the whole paper p.4: rain-> train greatly influence -> greatly influences p5: typo performace make a uniform bib: whole first name or abbr. , no URL, etc. p6: the weight -> the set of weights add the specification that the noise is Gaussian the sentence "the training set and testing set of ..." is used twice, remove one. p7 Table 1: the perf of DnCNN-B is 29.16 and not 29.15 for sigma 25, right?