Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: Most of the prior works for blind image super-resolution design explicit degradation estimation, which is often infeasible due to lack of proper degradation labels. Also, there might not exist a universal degradation model to capture various practical degradation types, such as blur, noise, compression, and platform instability which makes this problem particularly interesting. This paper proposes a method to learn an implicit degradation model leveraging learned representation in a teacher-student model. One of the important highlights of this paper is the generalization to various (real-world) degradation settings. Experiments show comparable or better performance than some existing models on commonly used datasets. The experiments on AIM19 and NTIRE2020 do not offer a significant gain over prior works which have not been included due to some reasons. The proposed approach is an amalgamation of previously known methods in related disciplines. 

strength_and_weaknesses: **Main Review**

**Major Strengths**

Real-world blind super-resolution with a combination of multiple unknown degradation is a challenging problem to deal with using explicit degradation models. This paper proposes to learn an implicit degradation model by leveraging a previously known method called knowledge distillation in a teacher-student setup. 

***Experiments***

The experiments are comprehensive and provide comparable or better performance than compared methods. The proposed method is computationally efficient in general. 

Experiments on various datasets with different degradation models show promising results. 

The authors demonstrate generalization to several degradations which is an important highlight of this work. 

**Weaknesses**

The main idea is an amalgamation of previously known methods, such as knowledge distillation and efficient super-resolution using GANs.

The loss function consists of standard objective functions with several regularization parameters. The authors should address how to tune all these parameters in large-scale real-world blind super-resolution tasks.

Table 3: Are the compared methods really the state-of-the-art for real-world SR on AIM19 and NTIRE2020?

In the literature, the results of DASR on AIM19 are reported to be better than the results obtained by the proposed method KDSR. In my opinion, the authors should clarify how the compared methods are SOTA, maybe in terms of the training procedure, e.g., paired or unpaired learning or appropriate metrics to make a proper assessment of the baselines. 

There is a disparity between the perceptual metrics, such as FID and LPIPS of popular SR methods including DASR (also the winner of AIM19 challenge FSSR) and the distortion metrics, such as PSNR and SSIM. As a result, there is a trade-off between perceptual and distortion quality. I wonder whether the disparity exists in the proposed method KDSR. If yes, how to overcome such issues?

It is well-known that quantitative improvements in terms of PSNR and SSIM do not necessarily indicate qualitative improvements, especially for the tasks under consideration. This is evident from Table 1, 2, 3, 4, and 5 of the main paper because most methods have similar scores. To make a fair comparison with baselines, I suggest the authors include LPIPS, FID or other image quality metrics on all the datasets.

Figure 8: Are the results generated for isotropic or non-isotropic Gaussian kernels?
clarity,_quality,_novelty_and_reproducibility: The paper is nicely structured and the presentation is clear. The experimental details are provided along with the source code to foster reproducibility. See main review for quality and novelty of the work.

summary_of_the_review: This paper proposes a method to learn an implicit degradation model leveraging learned representation in a teacher-student model. One of the important highlights of this paper is the generalization to various (real-world) degradation settings. Experiments show comparable or better performance than some existing models on commonly used datasets. The experiments on AIM19 and NTIRE2020 do not offer a significant gain over prior works which have not been included due to some reasons. The proposed approach is an amalgamation of previously known methods in related disciplines. 

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I thank the authors for their detailed response. I think the paper is in a good shape now, and it'd be interesting to many at the conference. Based upon the discussion, I plan to rate this as marginally above the acceptance level.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper introduces a method for blind single image super-resolution. Contrary to the mainstream the idea of this method is to design an implicit degradation estimator that can extract information regarding the degradation without having an explicit model. This is done using ideas from Knowledge distillation where first a teacher model is trained using paired LR/HR images and then a student model is trained only using LR images to predict the same degradation representation. 
strength_and_weaknesses: Strengths:
1. The paper introduces an interesting idea to address a highly relevant practical problem (blind real image super-resolution)
2. The paper presents many experiments and ablation studies showing that the method is robust and produces state-of-the-art results in terms of distortion metrics (PSNR).
3. The method is well detailed, and well motivated.

Weakness:
1. Presentation is cumbersome. It is hard to disentangle the contributions of the paper since everything is mixed together. Details about the architecture are given as long as the mathematical formulation. It would much better if the paper first present a mathematical formulation of the problem (the training, the loss functions, the goals), and later present the architecture and the details about the modules. In the current formulation is extremely hard to distill the ideas and understand what is new and what is not.
2. All the results are with benchmarks that use simulated degradations. It would be nice to see results (visual) on real images captured by any camera since the paper is claiming that addresses the real image super-resolution problem.

clarity,_quality,_novelty_and_reproducibility: The idea is neat and the paper presents a method that produces nice results (on the shown benchmarks). The presentation is a little disorganized and hard to follow, which hinders the contributions and make it hard to analyze the real novelty and contributions.
summary_of_the_review: This paper presents an interesting idea of using knowledge distillation to learn the degradation operation on the blind single image super-resolution task. The method produces very good results in the shown benchmarks. The main issue is the presentation. It is hard to disentangle the mathematical formulation from the implementation as everything is mixed together. Nonetheless I think this paper introduced a novel idea so I'm leaning towards accepting the paper. But, I would like to see a better presentation where the mathematical formulation (e.g., the abstract description of the modules, the goals of each module, the loss functions) is given in a separate section as the details about the architecture (and the exact implementation of the modules). I would like to see also results on real images (not from a benchmark). For example results on any image of the Open Image Dataset and a visual comparison in terms of artifacts (since no metric will be available).

After Rebuttal.
The authors addressed the concerns raised during the first round of reviews, and updated the manuscript accordingly. I'm leaning towards acceptance and thus I keep my original score.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes to use a knowledge distillation based to learn an implicit representation of degradations. The proposed method can handle complex degradation and does not need explicit degradation supervision. The proposed method finally achieve better results on several synthesized datasets. 
strength_and_weaknesses: 1.  The proposed method is novel and performs well on synthetic datasets.

2. The authors claim that the proposed KDSR implicitly estimates a degradation representation. While there are no experiments analyzing the estimated representations. The authors are suggested to compare the estimated degradations of images with the same/similar/different degradations. If the authors' claim stands, images that have different content but the same degradations will also have a similar estimated representation.

3. The experiments are performed on synthetic datasets. The authors are suggested to verify their methods on real-image datasets, such as the track2 of NTIRE2020. 

4. It will be better if the visual results provide comparisons of the whole images, since the degradation of the LR image may be spatially variant, while the estimated degradation representation is spatially invariant.
clarity,_quality,_novelty_and_reproducibility: The idea is interesting and clearly illustrated. The provided details seem to be adequate enough to re-implement the experiments. 
summary_of_the_review: This paper proposes a novel idea to implicitly estimate the degradation. While the experiments do not fully explore the estimated degradations and verify the method on real-image datasets. I will consider changing my opinion if the authors can provide more experiment details in the rebuttal phase.

--Post Rebuttal--:
The authors provide more persuasive experimental results and the response addresses most of my concerns. I would like to improve my score.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a method for blind SR in which a teacher network is used to estimate degradation from LR and HR to guide the student network to get the degradation from just LR. The experiment results show the proposed method performs better than existing methods (especially for the synthetic experiments).
strength_and_weaknesses: Strength:
The idea that implicitly estimating degradation and then using it for blind SR is reasonable.

Weaknesses:
1. Estimating a degradation and then using it for blind SR is not new, the novelty of the proposed method resides in estimating it implicitly. However, most of the experiments only consider blur kernel, SR and noises. They are not sufficient enough to validate the advantages of the proposed method over existing arts. Since these degradations can be easily simulated and the settings are easily treated as GT to be learned from a network and used in the following SR network (as explicit degradation). More complicated degradations should be considered to validate the effectiveness of the proposed method.
2. According to the results of Table 3, the proposed method does not have obvious advantages relative to other methods (especially BSRGAN).
3. For Table1 and Table2, are the compared methods trained on the same training set of the proposed method? If not, the comparison is unfair.
clarity,_quality,_novelty_and_reproducibility: This paper is easy to read. The novelty of the proposed method is somehow marginal. The only new thing is implicitly estimating degradation.
summary_of_the_review: The novelty is somehow marginal and the experiments cannot fully demonstrate the necessity of the proposed method.

After rebuttal, the authors address most of my concerns. I lean to accept this paper as long as the authors add more experiments and explain the reason that the proposed method has good generalization ability.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper introduces a new method for blind single image super-resolution. The authors design an implicit degradation estimator that can extract information about degradation without having an explicit model. This is done by using knowledge distillation where first a teacher model is trained using paired LR/HR images and then a student model is trained only using LR images to predict the same degradation representation.  Experiments show that the proposed method can achieve better results on several synthesized datasets. The paper received a total of 4 valid reviews. The rebuttal successfully addressed all the concerns from the reviewers, and in the final comments, all reviewers agree with the novelty, good experiments, as well as the presentation of the paper, and recommend accepting the paper. Additionally, the authors have made substantial revision on the paper by taking into account all the suggestions from the reviewers and including extra experimental results. The AC appreciates the efforts of the authors in improving the paper during the rebuttal and agrees with the judgements from the reviewers, thus recommending accepting the paper because of its novelty and solid experiments.   

justification_for_why_not_higher_score: The paper is a borderline paper, but the authors addressed all the concerns and made substantial revision on the paper by including extra experiments and following the suggestion provided by the reviewers. It is good to accept it as a poster.  
justification_for_why_not_lower_score: The authors addressed all the concerns and made substantial revision on the paper by including extra experiments and following the suggestion provided by the reviewers. It is good to accept it as a poster.
Decision: Accept: poster