Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper proposes a denoising method for hyperspectral images in a supervised and unsupervised manner. The author proposed a trainable sparse coding model with two-steps dictionary learning. Their method can also process different HSI with different number of bands. The evaluation outperforms the baselines in the majority of scenes. 
main_review: 1. Originality: Hyperspectral denoising is an interesting topic. It is well known that hyperspectral cube in nature is sparse and can be decomposed into a few basis. This is why we use dictionary learning for hyperspectral restoration and reconstruction. The related work is adequately cited. It's kind of a novel combination of LISTA algorithm, dictionary learning, and band-dependent noise analysis.  
2. Quality: The majority of their claim is supported by experimental results. It is a complete work that covers different noise patterns, blind or non-blind denoising, a variety of public datasets, synthetic and real noise. For the experiment design, as the authors claim their key contribution is based on the extension of dictionary learning, they should compare with hyperspectral dictionary learning denoising [12]. Also, there could be another ablation study of applying dictionary learning along wavelength axis slices to support their band-dependent noise assumption in real data, and the necessity of their method. 
3. Clarity: It is well written and organized. It has a sufficient description of details of experiments and comprehensive visual and quantitative results.  
4. Significance: The results are important. If my concern raised in quality (2) can be clarified, it is a strong experimental evidence of superior performance among deep learning based methods. Others are likely to use and build based on this idea. The LISTA algorithm also facilitates the usage of GPU and speeds up the inference, as shown in Table 2. This is an advantage for large-scale deployment. 
limitations_and_societal_impact: lthough the technical contribution is limited, I am still surprised that this simple two-stage dictionary learning method can outperform state-of-arts. If the authors compare their methods with traditional dictionary learning denoising on 2D-profiles of the hyperspectral cube, it's worth accepted as an experimental paper.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The paper proposes a deep learning based algorithm for hyperspectral image restoration that is interpretable, in the sense that the learning process separates the structure of the spatial and spectral dimensions. This allows the method to leverage the significant amount of available data for the spatial dimensions (e.g, image denoising). The method is computationally efficient.
main_review: There are many efforts in ML applied to imaging that have separated the learning of the spatial and spectral structure, as listed in "Benchmarked models"; however, the comparison with these baselines is fairly limited to one experiment in the supplemental material.

It is not sufficiently clear what the interpretability of the proposed method entails. How is the result of the restoration interpreted to obtain additional information? The fact that different dimensions are identified to have different known structures may not fit the definition of interpretable ML, which I understand to be that the algorithm provides knowledge in the structure it identifies; it seems to me here that the structure is already enforced in the learning model.

The approach of Section 3.4 is designed for nonuniform noise, but there is nothing intrinsic in it regarding hyperspectral imaging.

The computational efficiency does not account for training time. This would give a sense of how the training complexity compares to the computational complexity of the other algorithms and at what scaling point the algorithm becomes efficient across the board.

Minor typos: Table 1 caption "first two" -> "first four".
limitations_and_societal_impact: A single limitation is listed. The impact describes applications desired and undesired by the authors.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: The authors propose a denoising technique for hyperspectral images that is a combination of learning and model-based techniques. In particular, a dictionary learning technique using low-rank and sparsity priors is developed.
main_review: Overall, the paper is well-written and provides all the necessary backgroud to follow the explanation of the method. The problem of denoising hyperspectral images is important and often neglected by the community due to the challenges in finding large datasets. The necessity to carefully exploit spatial and spectral correlation and specific kinds of noise also make it significantly different from traditional RGB image denoising. The authors address all these challenges leveraging components which are in part well-known (e.g. the use of LISTA) but overall in an innovative manner. Addressing generalization of a single model to multiple sensors is also interesting in itself. 

Experiments cover a wide range of noise models and state-of-the-art methods and are adequate at characterizing the performance of the proposed method. However, I would have liked to see additional metrics besides SNR and SSIM, in particular regarding spectral quality (ERGAS, SAM, ...).
limitations_and_societal_impact: The authors addressed the impact of their work.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper proposed a trainable spectral-spatial sparse coding model for hyperspectral image restoration. This paper proposed a hybrid approach based on sparse coding principles that retain the interpretability of classical techniques encoding domain knowledge with handcrafted image priors, while allowing to train model parameters end-to-end without massive amounts of data. The proposed model is claimed to be computationally efficient, and produce state-of-the-art restoration results.
main_review: Trainable sparse coding has been utilized in nature image processing. But as far I know, this should be the first paper to introduce trainable sparse coding to hyperspectral image processing.

As an application work of trainable sparse coding for the hyperspectral image restoration, I have the following comments, regarding the review of the past work, the novelty of the proposed method, and the experimental results.

1. Regarding hyperspectral image restoration, low-rank matrix/tensor methods are the representative classical methods and achieved state-of-the-art performance. However, most of the related works are missing. 

2. Very recently, deep learning-related methods have been widely used for hyperspectral image restoration. But what are the main challenges of deep learning for hyperspectral restoration? What is the progress of the related works? The authors summarized that insufficient training dataset is one challenge of the related topic. But many works have contributed to this, which are disappeared in the introduction of the related work.

3. The introduction of the proposed method is unclear. Why the proposed trainable sparse coding is suitable for hyperspectral restoration? The insight analysis is insufficient. Furthermore, it is hard to understand the proposed method from figure 1, and realize the proposed method from figure 1. I can understand, the trainable sparse coding can provide interpretability. But what is new for hyperspectral processing society?

4. It is better to choose the latest tensor-based methods for comparison to demonstrate the advantage of the proposed method. The illustrated results are also unreliable. As reported in the reference paper, QRNN3D can achieve 40.23dB for the case ICVL dataset with noise 50. Furthermore, the tensor-related methods can easily achieve 35dB for the case of Washington DC Mall with noise intensity 50. However, the reported results are lower than those from the reference papers.


limitations_and_societal_impact: Please refer the main review
review_title: null

Meta-review: This work presents a combined sparse-coding and deep learning approach to achieve practical and efficient de-noising of hyperspectral images.  Following a discussion with the reviewers, the authors were able to clarify a number of key points relating to compute time and the relationship to competing methods. More importantly, the authors fixed an error identified by one reviewer and have indicated new results that will be included instead of those currently in the paper. With these corrections, the reviewers have updated their scores and I therefore recommend this work be accepted to NeurIPS. 
Decision: Accept (Poster)