Residual Non-local Attention Networks for Image Restoration
In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features. To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts. Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block. The trunk branch is used to extract hierarchical features. Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions. The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map. Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network. Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution. Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually.
 The authors propose a residual non-local attention net (RNAN) which combines local and non-local blocks to form a deep CNN architecture with application to image restoration. The paper has a compact description, provides sufficient details, and including the appendix has an excellent experimental validation. The proposed approach provides top results on several image restoration tasks: image denoising, demosaicing, compression artifacts reduction, and single image super-resolution. The main weakness of the paper is the limited novelty, as the proposed design builds upon existing ideas and concepts. However, up to some point all the new ConvNet designs can be seen as incremental developments of the older ones, yet they are needed for the progress of the field. I would suggest to the authors the inclusion of related works such as: Timofte et al., "NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results", CVPRW 2018 Wang et al., "A fully progressive approach to single-image super-resolution", CVPRW 2018 Note that DIV2K dataset was introduced in: Agustsson et al., NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study, CVPRW 2017 also, the more recent related works: Blau et al., "2018 PIRM Challenge on Perceptual Image Super-resolution", ECCVW 2018 Zhang et al., "Image Super-Resolution Using Very Deep Residual Channel Attention Networks", ECCV 2018 Also, I would like a response from the authors on the following: Why not using dilated convolutions instead of or complementary with the mask branch or other design choices from this paper?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Residual Non-local Attention Networks for Image Restoration
In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features. To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts. Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block. The trunk branch is used to extract hierarchical features. Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions. The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map. Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network. Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution. Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually.
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 authors propose a residual non-local attention net (RNAN) which combines local and non-local blocks to form a deep CNN architecture with application to image restoration. The paper has a compact description, provides sufficient details, and including the appendix has an excellent experimental validation. The proposed approach provides top results on several image restoration tasks: image denoising, demosaicing, compression artifacts reduction, and single image super-resolution. The main weakness of the paper is the limited novelty, as the proposed design builds upon existing ideas and concepts. However, up to some point all the new ConvNet designs can be seen as incremental developments of the older ones, yet they are needed for the progress of the field. I would suggest to the authors the inclusion of related works such as: Timofte et al., "NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results", CVPRW 2018 Wang et al., "A fully progressive approach to single-image super-resolution", CVPRW 2018 Note that DIV2K dataset was introduced in: Agustsson et al., NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study, CVPRW 2017 also, the more recent related works: Blau et al., "2018 PIRM Challenge on Perceptual Image Super-resolution", ECCVW 2018 Zhang et al., "Image Super-Resolution Using Very Deep Residual Channel Attention Networks", ECCV 2018 Also, I would like a response from the authors on the following: Why not using dilated convolutions instead of or complementary with the mask branch or other design choices from this paper?
assistant
 The authors propose a residual non-local attention net (RNAN) which combines local and non-local blocks to form a deep CNN architecture with application to image restoration. The paper has a compact description, provides sufficient details, and including the appendix has an excellent experimental validation. The proposed approach provides top results on several image restoration tasks: image denoising, demosaicing, compression artifacts reduction, and single image super-resolution. The main weakness of the paper is the limited novelty, as the proposed design builds upon existing ideas and concepts. However, up to some point all the new ConvNet designs can be seen as incremental developments of the older ones, yet they are needed for the progress of the field. I would suggest to the authors the inclusion of related works such as: Timofte et al., "NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results", CVPRW 2018 Wang et al., "A fully progressive approach to single-image super-resolution", CVPRW 2018 Note that DIV2K dataset was introduced in: Agustsson et al., NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study, CVPRW 2017 also, the more recent related works: Blau et al., "2018 PIRM Challenge on Perceptual Image Super-resolution", ECCVW 2018 Zhang et al., "Image Super-Resolution Using Very Deep Residual Channel Attention Networks", ECCV 2018 Also, I would like a response from the authors on the following: Why not using dilated convolutions instead of or complementary with the mask branch or other design choices from this paper?