PONE-D-23-44021Multi-focused image fusion algorithm based on multi-scale hybrid attention residual networkPLOS ONE

Dear Dr. Liu,

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Khan Bahadar Khan, Ph.D

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: N/A

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The current work proposed the multi-focus image fusion network based on deep learning for improving the detection. Some comments need to be addressed before its publication:

How to weight the two different components in the loss function needs to be further explained and demonstrated.

The evaluations of Tables 2 and 3 need more explanations especially on these quantitative paramters demonstrated.

Reviewer #2: The paper proposes a multi-focused image fusion algorithm based on multi-scale

hybrid attention residual network. Though I found the contribution very interesting, the following problems need to be addressed.

Questions and observations are summarized (not in order of importance) in the following items:

1. The content of this paper is incomplete, all figures are not provided.

2. Please cite relevant works from the past 3 years in the literature section.

3. in formulas 3 and 4, the symbols used differ from those appearing in the formulas，for example，ϕCAM and φCAM. It is suggested to change for consistency to facilitate readers understanding.

4. Why didn't the symbol ϵ(·) below formula 4 and the symbol ϕ below formula 10 appear in both formulas?

5. What does S' mean in formula 10? Please provide an explanation below formula 10 for better understanding by the readers.

6. Is Lsimm in contribution 3 the same as Ls in formula 13? If not, please provide clarification.

7. Eight methods were proposed in the comparative analysis, but only seven are presented in Table 1. Is the GF-IMF method not included for comparison?

8. The ablation study is insufficient. The authors only provide the ablation experiment of Loss function.

9. All parameters in formulas should be described and explained.

10. Lp and Lsimm are the classical loss function. In view of this point, the contribution 3 can not be regarded as the innovation.

Reviewer #3: Here are my detailed critiques:

1. The paper claims robust performance across various datasets but lacks rigorous testing on diverse, real-world scenarios. The primary reliance on the MS-COCO2012 dataset raises concerns about the model's ability to generalize to different types of multi-focus images, especially in more complex or less controlled environments.

2. While the paper introduces MSHRN, it does not sufficiently differentiate this approach from existing methods. The innovation in combining multi-scale features and attention mechanisms needs to be better highlighted, or the incremental nature of this improvement should be more transparent.

3. The role and effectiveness of the Up-and-Down Sampling Projection (UDP) module are not convincingly established. How does this module specifically contribute to the network's performance, especially in comparison to other edge-enhancement techniques?

4. While the paper includes comparative analysis with several methods, the selection of these comparative algorithms appears limited. Inclusion of more recent and diverse state-of-the-art methods would better position the paper's contributions within the current research landscape.

5. The reliance on subjective evaluation is a significant drawback. More rigorous quantitative metrics are needed to objectively assess the performance of the proposed method. Also, the manuscript does not provide a clear rationale for the choice of the specific metrics used.

6. The paper lacks detailed explanation and justification for the choice of network architecture, including the number of layers, filter sizes, and specific configurations of the MSHRN and UDP modules. Such details are crucial for reproducibility and critical assessment.

7. The combination of structural similarity function loss (Ls) and pixel function loss (Lp) is not novel. A more thorough justification for this specific combination and its parameter tuning is needed, including why this combination is more effective than others.

8. The paper does not provide sufficient theoretical grounding for the proposed method, particularly in how the MSHRN and UDP modules contribute to the overall performance improvement. More theoretical analysis or insights into why this architecture works effectively for multi-focus image fusion is needed.

9. The manuscript does not adequately address the computational efficiency of the proposed method, which is a crucial factor in real-world applications. Information about the model's computational complexity, memory requirements, and execution time would provide a more comprehensive assessment of its practical viability.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Multi-focused image fusion algorithm based on multi-scale hybrid attention residual network

PONE-D-23-44021R1

Dear Dr. Liu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Khan Bahadar Khan, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: (No Response)

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: (No Response)

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: (No Response)

Reviewer #2: No

Reviewer #3: (No Response)

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: (No Response)

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine Fine

Reviewer #2: (No Response)

Reviewer #3: The author have addressed all of my concerns. I have no more comments. I think the paper will benifit the scientific community.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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