A Deep Unfolding Network for Multispectral and Hyperspectral Image Fusion

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper has the following issues:

1.It is not recommended to quote too many references in one sentence, such as line 29 “and deep learning (DL) approaches[13–25]”.

2.The drawing is not standardized, as shown in Figure 1 (c) where the arrow passes through the formula. It is recommended that the author make modifications.

3.When writing the second part, it is recommended to first provide the overall framework of this article, which is Figure 1, and then introduce the various parts of the algorithm, so that readers can grasp the outline of the paper.

4.In section 3.1, “Obviously, the fused results of our MHF-CSCNet are 303 satisfactory. At the same time, we show the spectral vector at a certain position in Fig. 7(a)...”it is recommended to place the analysis of Figure 7 in front of Figure 7 instead of below Figure 2. The analysis and interpretation of other figures and tables also have the above-mentioned issues, and it is recommended that the author make modifications.

Comments on the Quality of English Language

Minor editing of English language required.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This paper proposes a novel model-based deep neural network for the MS/HS fusion task. Firstly, a new MS/HS fusion observation model based on convolutional sparse coding (CSC) is proposed. Then, a proximal gradient algorithm is designed to solve this model, and the algorithm is unfolded into a network. The unfolded network has a clear physical interpretation since each module corresponds to a specific operation of the algorithm. Experimental results conducted on some benchmark datasets demonstrate that the proposed network can obtain comparable or better quantitative and qualitative performance compared to other methods. However, there are still some issues that require further revision. Specific comments are given below:

1.     Your manuscript needs careful editing and particular attention to English grammar, spelling, and sentence structure.

2.     Please discuss the difference between the proposed method and MHFnet.

3.     It is recommended that the author add references related to deep unfolding method in the introduction.

4.     Another problem with this paper is lack of sufficient explanation of the simulation results. You need to explain your simulation results in detail and why you got such results.

5.     How are the parameters \lambda_1, \lambda_2, and  \lambda_3 set?

Comments on the Quality of English Language

n/a

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Please see my attached file. 

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Accept in present form.

Comments on the Quality of English Language

Minor editing of English language required.

Reviewer 3 Report

Comments and Suggestions for Authors

The author satisfactorily addressed all my concerns and comments. This version is acceptable to me. Congratulations! Nice work!