Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: this paper proposes improving the self-supervised denoising in 2 frameworks (noise2same and noise2self) by replacing the convolutional backbones with a swin transformer model (swinIR). This work is validated on 3 publicly available datasets - bsd68, HanZi and ImageNet
strengths_and_weaknesses: a) Clarity: comments below
Strengths: 

- Detailed experiments and ablation studies have been performed on a set of publicly available datasets with varying sizes. 
- It provides a helpful reference to assess the computational costs involved while using a transformer-model versus conventional convolutional networks (u-net)
- the paper is well-structured though there is some room for improvement in the methods and conclusion sections.

b) Correctness: some comments/discussion points that I am raising here

1. noise2same: as pointed out by the authors, unet outperforms swinir on the hanzi dataset. for hanzi dataset, did the authors try adding more noise or blurring to see if reduction in high-frequency content leads to any changes in the results ? 
Related: I am asking this because the authors conclude that 'SwinIR is capable of restoring complex high-frequency details' - what kind of complexity are you referring to here ?

2. noise2self: for both hanzi and imagenet, noise2same (xie, 2020) and unet outperforms swinIR (both psnr and ssim). Would the authors hypothesize why this is the case (at least for imagenet) when the results are the opposite for the bsd68 dataset ?

3. As a discussion, do the authors think that there is 'sweet spot' in terms of the psnr-ssim tradeoff that can be considered as a guideline (example: beyond a certain increase in psnr, the ssim begins to deteriorate). If yes, then it would be great to have this in the paper.

4. do the authors feel that given the significant computational overhead involved vs the minor changes, it is feasible to use swin transformers for these particular cases ? (also please look at the references given in the 'suggested changes' section)

c) Reproducibility: Code not provided. The datasets used are publicly available.

d) Follows basic requirements: Yes

suggested_changes: 1. Please address the points raised in the previous section (correctness)

2. figure 1 has been supplemented with additional examples in figure 4. It would have been nicer to have a few regions zoomed in (as done in fig.1) to better visualize the extent of smoothing and the retaining of structural similarity

3. to modify the conclusions section: for feasibility of using transformers for 3d data, please take a look at the following papers (which I guess you may already have):
a. https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Self-Supervised_Pre-Training_of_Swin_Transformers_for_3D_Medical_Image_Analysis_CVPR_2022_paper.pdf

b. https://www.cell.com/patterns/pdf/S2666-3899(22)00083-6.pdf

If addressed suitably, I would then increase the score assigned to CCR.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper study the usefulness of Swin transformer in self-supervised image denoising by replacing the fully convolutional blocks with the Swin transformer block. The authors tested the proposed approach with two image denoising methods and on three different datasets. 
strengths_and_weaknesses: - Clarity: The paper is written clearly and relevant literature is discussed.

- Correctness: As claimed, the paper discussed the advantages of using Swin transformers over convolution block and discussed its limitations in terms of throughput and resources.
    - I am concerned about the usefulness of the proposed approach as table 1 shows mixed results with SwinIR as the backbone.
    - Given that SwinIR as backbone requires lots of resources as compared to convolution network how one would justify its use for the methods where SwinIR performance is lower than U-Net?
    - Further, the performance gain of SwinIR over U-net is very small, making me doubt the usefulness of SwinIR.

- Reproducibility: No code and data is provided

- Follows basic requirements: yes
suggested_changes: - Experiments: 
    - Performance statistical significance test to better understand if the slight performance gain with SwinIR not because of a random chance. 
    - I would also reason about why the proposed change does not work for all three datasets.
review_title: null

Decision: Invite to archive