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.
 The paper proposes a restoration method based on deep reinforcement learning. It is the idea of trainable unfolding that motivates the use of Reinforcement learning, the restoration unit is a SoA U-Net. Remarks * The author seems to make strong assumptions on the nature of the noise and made no attempt to understand the nature of the learning beyond a limited set of qualitative example and PSNR. * Even if the experimental protocol has been taken from prior work, it would have been appreciated to make it explicit in the paper, especially as ICLR is not a conference of image processing. Indeed, It would have made the paper more self-sufficient. * Second 2 describing the method is particularly hard to understand and would require more details. * In the experimental section, the authors claim that "These results indicate that the restoration unit has the potential to generalize on unseen degradation levels when trained with good policies". It would have been important to mention that such generalization capability seems to occur for the given noise type used in the experiments. I didn*t see any explicit attempt to variate the shape of the noise to evaluate the generalization capability of the model. In conclusion, the paper proposes an interesting method of image denoising through state of the art deep learning model and reinforcement learning algorithm. The main difference with the SoA on the domain is the use of a diffusion dynamics. IMHO, the paper would need more analysis and details on the mentioned section.
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.
user
<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>



 The paper proposes a restoration method based on deep reinforcement learning. It is the idea of trainable unfolding that motivates the use of Reinforcement learning, the restoration unit is a SoA U-Net. Remarks * The author seems to make strong assumptions on the nature of the noise and made no attempt to understand the nature of the learning beyond a limited set of qualitative example and PSNR. * Even if the experimental protocol has been taken from prior work, it would have been appreciated to make it explicit in the paper, especially as ICLR is not a conference of image processing. Indeed, It would have made the paper more self-sufficient. * Second 2 describing the method is particularly hard to understand and would require more details. * In the experimental section, the authors claim that "These results indicate that the restoration unit has the potential to generalize on unseen degradation levels when trained with good policies". It would have been important to mention that such generalization capability seems to occur for the given noise type used in the experiments. I didn*t see any explicit attempt to variate the shape of the noise to evaluate the generalization capability of the model. In conclusion, the paper proposes an interesting method of image denoising through state of the art deep learning model and reinforcement learning algorithm. The main difference with the SoA on the domain is the use of a diffusion dynamics. IMHO, the paper would need more analysis and details on the mentioned section.
assistant
 The paper proposes a restoration method based on deep reinforcement learning. It is the idea of trainable unfolding that motivates the use of Reinforcement learning, the restoration unit is a SoA U-Net. Remarks * The author seems to make strong assumptions on the nature of the noise and made no attempt to understand the nature of the learning beyond a limited set of qualitative example and PSNR. * Even if the experimental protocol has been taken from prior work, it would have been appreciated to make it explicit in the paper, especially as ICLR is not a conference of image processing. Indeed, It would have made the paper more self-sufficient. * Second 2 describing the method is particularly hard to understand and would require more details. * In the experimental section, the authors claim that "These results indicate that the restoration unit has the potential to generalize on unseen degradation levels when trained with good policies". It would have been important to mention that such generalization capability seems to occur for the given noise type used in the experiments. I didn*t see any explicit attempt to variate the shape of the noise to evaluate the generalization capability of the model. In conclusion, the paper proposes an interesting method of image denoising through state of the art deep learning model and reinforcement learning algorithm. The main difference with the SoA on the domain is the use of a diffusion dynamics. IMHO, the paper would need more analysis and details on the mentioned section.