Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes a real-world image denoising model with alternative optimization. The formulation is carefully discussed in order to ensure the validity of the proposed pipeline. This is an interesting approaching to solve the problem of image denoising. Experiments and ablation studies are sufficient to prove the superiority of the proposed method over other state-of-the-art methods and the efficiency of the designed modules.
strengths_and_weaknesses: Strengths:

1.	The method of alternative optimization is novel to image denoising. In order to adopt this method, the formulation of denoising is strictly discussed and the pipeline is carefully designed.

2.	The definition of the problem and the corresponding solution are clear. Instead of regarding NNs as a ‘black box’, the authors try to analysis the building reasons of every block.

3.	The experiments and ablation studies are sufficient. The performance of the proposed method is better than SotA methods clearly.

Weakness:

1.	The proposed method is seemingly an incremental method to DAN [1]. Although the pipeline is specifically designed for the task of image denoising, I think that it somehow lacks of novelty.

2.	I miss a general description of the total pipeline. Such a complex framework is a little bit hard to read. I suggest the authors to modify the texts of Sec. 2 and 3.

3.	The subjective results shown in the paper is not that impressive. I suggest the author choose clearer results to replace Fig. 4 and the upper row of Fig. 5.

[1] Z. Luo, Y. Huang, S. Li, L. Wang, and T. Tan. Unfolding the alternating optimization for blind super resolution. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, pages 5632–5643, 2020.

questions: 1.     The embeddings in the proposed Latent Space (LS) are seemingly just convolutional features of input images. Whether these latent embeddings have special semantics? If not, why the authors emphasize it in the paper (even in the title)?

2.	I notice that the proposed method uses improved channel attention (CIG-CA) and spatial attention (CIG-SA). Have the authors tried self-attention mechanism? If not, why?

3.	The formulation of the proposed method is seemingly weak while the performances are excellent. I totally agree that this is a great work, but I’d like to ask the authors that have you think about to submit your work to ECCV or CVPR? I think it would be more suitable for those conferences instead of NeurIPS.

limitations: The limitations are clearly addressed in supplementary materials. This work does not have any potential negative societal impact.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper proposed an enhanced latent space blind model based deep unfolding network, namely ScaoedNet, for complex real image denoising.  Experiments demonstrate the superiority of ScaoedNet over many SOTA methods.
strengths_and_weaknesses: Strength:
 
1. An enhanced model-based denoising cost function is proposed to implicitly optimize the ScaoedNet, which addresses the challenging task of manually designing the optimal operators and making the optimal process more interpretable. 

2. The effective NFAE layer leads to better results without increasing extra parameters.

Weaknesses:

1. The description of the total loss is not clear enough. 

2. Map noise image to space that better distinguish noise has been proposed in [1] and therefore cannot be made as a prominent contribution.

[1] 1. Cheng, Y. Wang, H. Huang, D. Liu, H. Fan, and S. Liu. Nbnet: Noise basis learning for 356 images denoising with subspace projection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4896–4906, Jun. 2021.

questions: Please clarify the difference between you and [1] on how to use the distinguishable space (named latent space or subspace), and the superiority of your usage. 
limitations: See in weakness.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: In this paper, the authors proposed a method for real image denoising. The authors designed the enhanced model-based denoising cost function by introducing latent space, noise information and guidance constraint. And then implement the proposed self-correction alternative optimization algorithm to minimize cost function via deep network. Besides, for higher performance, some optimized modules of subnetwork are proposed, including noise feature adaptive enhancement (NFAE) layer and feature multi-modulation attention residual block (FM2ARB).
strengths_and_weaknesses: Motivated by the advances in deep networks and relying on the rich body of the model based methods, authors propose a novel enhanced latent space (LS) blind model based deep unfolding network for real image denoising. Extensive experiments verify that this method achieves excellent performance on real image denoising. 
However, authors should pay more attention to clarity of the paper. For example, the abstract of this paper is too mess to emphasis the most important novelty. Author needs to write it in brief and appropriately. 
Besides, the layout and the visualization of some figures also need to be attached. 
e.g., the Figure 1 is not mentioned in the text but shown. In Fig.2, the name of subnetworks cannot be corresponded to the figure note. Readers still need to find each subnetwork’s name through article.
questions: 1. In ‘Analysis and Enhancement’ part, the author represents that LS encoding function can obtain high-dimensional image embeddings instead of limited squared error in low-dimensional image space. However, no clear theoretical reason and significance are indicated.
2. As mentioned in the article, LS scheme is the essential for the performance of this work. However, there is still no enough explicit explanation for its ablation study.
3. The author has already chosen the SSIM as one evaluation, why repeatedly adopt SSIM loss in the total loss function? 
4. Can author provide more comparison with other noise estimation network to further verify the effectiveness of NFAE layer?
5. Authors also can compare the proposed method with some more advanced traditional methods, e.g.: Exemplar-Based Denoising: A Unified Low-Rank Recovery Framework, IEEE Transactions on Circuits and Systems for Video Technology, 2020; Deep Convolutional Dictionary Learning for Image Denoising，CVPR, 2021.
limitations: In this paper, the method that authors proposed makes full use of the advantages of classical denoising methods and deep network, which no longer sticks to AWGN and contributes to real image denoising to a certain extent.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes an image denoising network based on latent space self-correction alternative optimization. A deep unfolding network implements alternative optimization, and state of the art denoising performance is achieved on multiple benchmark datasets.
strengths_and_weaknesses: Strengths: 
- the proposed method is backed with classic model based image restoration theory that provides strong regularization on the network design, which allows the proposed method to achieve state of the art performance with a modest model size.
- implementation detail of the proposed network is well documented for reproduction
- extensive ablation study is conducted to analysis each individual component of the proposed network architecture

Weakness:
- The experimental evaluation section lacks complexity and time cost analysis and comparison
- section 3 is filled with an overwhelming amount of implementation detail that are better suited for supplementals. It would be better to put emphasis on theoretic/algorithmic reasoning & insights, and put implementation details in section 4 or a dedicated subsection in section 3. Right now section 3 provides very limited insight into *how* the network implements and enforces those ideas.
questions: 1. Would raise score if inference complexity/time is on par with other state of the art methods.
2. Is the proposed model based framework applicable to other image restoration problems, e.g. deblurring, deraining, etc. ? This paper only discusses denoising and the value of the proposed methodology is therefore limited.
limitations: N/A
review_title: null

Meta-review: metareview: The paper under review introduces a deep unrolling network driven by a latent space blind model for image denoising. Although the network combines known components, it has novel elements and good algorithms, the experimental results are robust, the implementation details are rich, and the ablation research is extensive. Revisions and rebuttals addressed most of the reviewers' concerns, leading them to improve their scores. Therefore, I accept this paper.
confidence: Certain
Decision: Accept