PONE-D-22-22875An Optimized GAN Method Based on the Que-Attn and Comparative Learning for Underwater Image EnhancementPLOS ONE

Dear Dr. Zhou,

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Yiming Tang, 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: Yes

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

Reviewer #1: Yes

Reviewer #2: No

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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: Yes

Reviewer #2: Yes

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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: No

Reviewer #2: 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 work in the paper looks solid with good results. Unfortunately the English could be improved.

- In a large number of places the paper talks about contrast learning rather than contrastive learning

- Line 172 refers to Fig 3-2. There is no such figure.

- Fig 3 has missing labels for G(I_X), I_X, I_Y, F_X, F_Y, A_g and A_{QA} which makes it hard to understand

- Fig 4 also missing labels, e.g F_X and F_Y

- In the caption of Fig 4 it uses "mg" presumably for "A_g"

- Line 183: "Our another" should be "Our other"

- Line 194: The sentence ending "not important to ." seems unfinished

- Line 250: "We use and a" should be "We use a"

Generally the paper is let down by the English which makes it quite hard to understand.

Reviewer #2: - Line 92 « Li et al [21] », Reference 21 is not authored by Li et al. Please correct.

- Lines 96-98: It is said that “training on real data can produce visually satisfactory results”, but also that “they often produce unreal results because the reference image is not the actual ground truth” How real data could not be actual ground truth ?

- Line 172 “The background theory is shown in Figure 3-2”.There is no Figure 3-2. Please correct

- Fig.2 is not documented in the core of the manuscript. We don’t know exactly what the purpose of this figure is. Please specify where the original underwater images and the restored images are.

- Qualitative results on the HICRD test dataset (fig. 5) and Quantitative results are given for EUVP dataset (table 1). It would be interesting to have the two evaluations for the same dataset to compare the qualitative and quantitative results.

- In figure 5, the authors claim the superiority of their method compared to traditional restoration methods. But the CUT, CycleGAN and CWR methods are visually sometimes more satisfying than the proposed method.

- Tables1, 2, 3, the experimental data,(EUVP, HICRD, UIEB) are presented as “synthetic underwater images”, whereas in Datasets paragraph (p.9/15), they are not.

- The problem of time efficiency is mentioned in page 10 (“speed and memory” paragraph) but real time processing is not tackled, it is pivotal for robots’ autonomous operations. Thus, it is essential to develop a real-time and adaptive method for underwater visual restoration. What is the real-time efficiency of the proposed method and how could it be improved.

- To provide complete information, the legends of the tables of results and the figures of results must specify the data used for learning and the test data.

- Table 4 should be more clearly described. What is the difference between A (Ordinary GAN Network) et C (GAN Network) ?

- The proposed attention-querying mechanism preserves the simple design of CUT and does not add any model parameters. However, in reference 41 (Park T, Efros AA, Zhang R, Zhu JY., European conference on computer vision. Springer; 2020. p. 319–345), the authors show results (see Fig. 3 in the cited article) with two failure cases. In the first case, they were unable to identify the unfamiliar pose of the horse and instead add texture to the background. In the second, the method added a tongue to the dog that does not exist. Have you been faced with such failure situations. If not, could you explain why.

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Reviewer #1: No

Reviewer #2: Yes: Abdelkader Boulanouar

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An Optimized GAN Method Based on the Que-Attn and Contrastive Learning for Underwater Image Enhancement

PONE-D-22-22875R1

Dear Dr. Zhou,

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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Kind regards,

Yiming Tang, 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: All comments have been addressed

Reviewer #2: All comments have been addressed

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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: Yes

Reviewer #2: (No Response)

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

Reviewer #1: Yes

Reviewer #2: (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: Yes

Reviewer #2: (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: Yes

Reviewer #2: (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: The authors have made substantial changes improving the paper. I am now happy that this is of sufficient quality to be accepted.

Reviewer #2: (No Response)

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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: Yes: Abdel-Kader Boulanouar

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