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 is on spectral reconstruction from snapshot CASSI. It is based on the linear inverse model, and solved by merging model-based and learning-based methods in the framework of deep unrolling network. Compared with existing methods, memory-assistant descent blocks and cross-stage attentive proximal subnetworks are proposed, together with a new loss term. Experiments show that it outperforms exiting unrolling based methods and end-to-end methods. 
strengths_and_weaknesses: Strong points: 
1.	To use deep unrolling for image/video restoration receives attention in recent years, and the authors proposed new add-ons to improve DUN for CASSI based spectral reconstruction. 
2.	Experiment results have shown that the proposed method achieves favorable results than existing iterative methods, as well as the latest end-to-end learning based methods. 

Weak points: 
1.	Initialization of the algorithm and its effect on convergence is not mentioned/discussed. 
2.	Model/algorithm complexity and running time is not mentioned, especially the advantages/disadvantages when compared with end-to-end methods.
3.	Many results are directly quoted from other papers. Is there any reason for that? At least ,there are some randomness for data augmentation. To quote results from elsewhere tends to be improper. 
4.	No quantitative comparison is conducted on real data. Maybe a real dataset with GT should be prepared first.
5.	Personally, the reviewer has severe concern about the real meaningfulness of trying to reconstruct spectra from CASSI. Given the spectral accuracy in Fig.2 and Fig4, most high-frequency details can not be recovered. To use several mosaiced filters can reach same level accuracy, yet has much better spatial resolution/details. Given this level of spectral details, reconstructed spectral curves are not meaningful for practical applications. One might directly use CASSI raw images for segmentation/recognition directly, without reconstruction. 

questions: Please refer to the weakpoints.
limitations: No potential negative social impact.
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: This paper presents a method for reconstruction a hyperspectral image from its snapshot measurements. 
strengths_and_weaknesses: This paper requires further improvements on paper organization. The technical contributions of the proposed approach and theoretical background to support the proposed algorithm are not clear enough from the paper. There are many places where the explanation is unclear. 

questions: Introduction should discuss the novelty and technical contributions of the proposed approach over previously proposed approaches.
Theoretical background to support the proposed algorithms should be clearly described. 
limitations: The technical contribution of the proposed approach and theoretical background to support the proposed algorithm are not clear enough from the paper.
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: The paper proposes a new unrolled neural network architecture for the problem of snapshot compressive hyperspectral imaging.
The architecture is inspired from proximal gradient descent, where both the gradient update and the proximal mapping steps are replaced by two recurrent modules.
The former (Memory-Assistant Descent) is based on ConvLSTM, and the latter (Cross-stage Attentive Proximal sub-network) uses a combination of convolutions, self-attention and triplet attention.
The authors compare against multiple recent approaches on several datasets.

strengths_and_weaknesses: # Strengths
* The method achieves very good performances on noise-free data
* The performances are compared against several recent methods
* Several datasets are used to show the superior performances of the proposed method
* Ablation studies are provided for multiple parts of the method, allowing to validate the overall significance of the main contributions

# Weaknesses
* All quantitative results shown are valid on noise-free measurements only.
  * No quantitative results are provided for noisy measurements, although the image formation model described in the introduction explicitly mentions the presence of noise on measurements. 
  * This is even more dissonant as the model is fine-tuned on synthetic **noisy** data (shot noise) before applying it to real data for visual assessment, which indicates that the noise-free setting is not very realistic.
* The authors claim that the visual results on real data is sufficient evidence of the good generalization of the method on real measurement data (i.e. with noise). This is not a convincing argument as it is very difficult to see any improvement of the proposed method over its competitors on real data.
* All quantitative results are provided without error bars.
* No visual comparison is provided against the most competitive methods (HDNet, MST-L) on real data.


questions: * Regarding generalization to real data:
  * As previously stated, visual comparison on real data is not provided against the most performant methods (HDNet, MST-L). This would help assessing the performance of the method.
  * A convincing argument would be to provide quantitative results on synthetic data **with noisy measurements**, similar to table 2 of the paper TSA-Net, where the authors argue that shot noise is realistic in this setting.
* There is no evidence that the unrolled architecture is beneficial with this method. An ablation study with a few different number of iterations/stages would be more convincing (including the limit case of a single iteration).
* Since both the Gradient Update and the Proximal Mapping modules benefits from using information across several stages/iterations, what is the justification for using different recurrent architectures (ConvLSTM and "Cross-stage self-attention") for tasks which are so similar in spirit ? If the authors disagree with this last statement, please explain why.
* The use of triplet attention for the CAP module is mentioned, but it would be helpful to develop a bit more why it is used here.
* It should be made clearer that the results presented in table 1 and table 2 are for noise-free synthetic data.
* What is the batch size used ?
* Minor:
  * The same notation is used to denote convolutions (eq. 10-13 and 15) and matrix multiplication (eq. 14). It would be clearer to denote the convolution operation with a dedicated symbol (see for example the ConvLSTM paper), and more coherent as the Hadamard operator has its own explicit symbol.
  * In eq 16, the symbol used for concatenation is ambiguous.
  * l. 202: it is unclear what the authors refer to as "padding number". Another formulation would be clearer.
  * l. 79: supposed to **be** (I guess)
  * eq. 10-11: missing term with **C^(k-1)**
  * l. 201: please provide citation for PyTorch
  * Table 1: 
    * the table is difficult to read. It would benefit from enhancing the separation between methods (spacing or horizontal lines).
    * the number of parameters of DNU should be in bold
  * fig. 2-3 of main paper and fig. 3 of supplemental: DGMP -> DGMSP ?
  * on visual comparison, it would be helpful to know which spectral channel are shown.
  * on visual comparison on real data, it would be helpful to display the zoomed region on the reference and snapshot, as it is done with the results
  * l. 149: decent -> descent


limitations: * The broader impact section is not included in the 9 pages limit. Instead it is addressed in the supplemental material.
* The authors do not address the fact that their quantitative experiments are only valid on noise-free measurements only.
* The authors adequately mention the limitations of the supervised framework, i.e. its dependency on the amount of data available.
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: This paper proposes a proximal gradient descent (PGD) based deep unrolling network for snapshot compressive hyperspectral imaging, which employs a memory assistant descent module for gradient descent and a sub-network with cross-stage self-attention for proximal mapping. The experimental results show that the proposed method outperforms the compared methods.
strengths_and_weaknesses: Strength
+ Deep unrolling network is an important technique for snapshot compressive sensing.
+ Modeling correlation between different iteration stages in deep unrolling network is a good idea to improve the performance.
Weakness
- Novelty. Memory assistant deep unrolling network has been proposed in previous work, such as [1]. The relationship and the difference should be discussed. Besides, the comparison between these two methods is also necessary.
- Interpretability. Deep unrolling network is famous for its interpretability, compared with conventional deep network. However, adding the connection between different iteration stages weaken the interpretability. Moreover, in this paper, employing ConvLSTM to model the correlation between gradient descent in different iteration stages, which does not follow the PGD iteration. Besides, for hyperspectral snapshot compressive sensing, employing other optimization methods, e.g, ADMM, always have a fast close-form solver for data-fidelity. Thus, gradient descent and further memory-assistant module may be unnecessary.
- Experiment. PSNR and SSIM only evaluate the spatial fidelity between reconstructed HSI and groudtruth. Spectral fidelity evaluation metrics, e.g., SAM and ERGAS, are also necessary, especially the proposed method proposes a spectral loss.
- Writing. The citation of RMSProp and Adam is lacking in line 61. There lacks blank between 0 and otherwise in line 195. Please checking the typos of this manuscript.


questions: Please see strength and weakness
limitations: Yes
review_title: null

Meta-review: metareview: The paper received mixed reviews with two weak accepts and two borderline rejects, making it a borderline case. Based on the reviews and the rebuttal and on their own reading of the paper, the area chair would like to make a few remarks:
 - the paper achieves good empirical results in terms of PSNR for the task of compressive HSI. This is the main strenght of the paper.
 - the code for reproducing the experiments is not provided. Even though this is not a critical requirement, this would definitely help assessing the quality of the experiments, given that the contribution is mostly methodological.
 - the method consists of modifying several components of classical deep unrolling networks. Some of them are related to existing work, such as the idea to use lstms to exploit past gradients, as discussed in the rebuttal. Such discussions should be included in the paper, even though this idea was not investigated for HSI in this prior work. More generally, the modifications of the DUN method seem quite generic and not specific to compressive HSI. Evaluating their effect on other HSI tasks would be helpful to get a better understanding of the importance of these modifications: are they effective beyond compressive HSI, beyond HSI. If not, why?
 - The rebuttal was useful. Numerous additional experiments were conducted. Yet, it would have been good to include them in a revised version of the pdf. 

At this point, it seems that the method is promising, but that a major revision of the paper is required, leading to a reject decision for NeurIPS this year.



 
confidence: Less certain
Decision: Reject