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Peer-Review Record

Novel Deep Level Image State Ensemble Enhancement Method for M87 Imaging

Appl. Sci. 2020, 10(11), 3952; https://doi.org/10.3390/app10113952
by Timothy Ryan Taylor, Chun-Tang Chao and Juing-Shian Chiou *
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Appl. Sci. 2020, 10(11), 3952; https://doi.org/10.3390/app10113952
Submission received: 17 April 2020 / Revised: 26 May 2020 / Accepted: 3 June 2020 / Published: 6 June 2020
(This article belongs to the Section Computing and Artificial Intelligence)

Round 1

Reviewer 1 Report

This paper presents an interesting approach for image reconstruction applied to the  Spitzer Space Telescope image reconstruction. 

To the statement "This paper addresses the issue of decreased performance in SSIM, MSE, and PSNR and introduces ISEE deeper decomposition levels 37 (Ln), while presenting an image reconstruction process algorithm deemed ISEE." Could you please include a comparison with previous reported methods and detail the advantages? That could improve the soundness of the paper. 

This paper is incredibly long, consider reducing the length, I'm aware that a big part are images, maybe consider include them in tables and reduce size. 

 

Author Response

Dear Reviewer 1:  Please see the attachment. Thanks for your kindness.

Author Response File: Author Response.pdf

Reviewer 2 Report

A new method of image reconstruction Image State Ensemble Enhancement. The proposed method is an improvement of the author's previous work. This paper addressed an important issue in the field however, some information is missing and a revision is required. Please consider my comments below to improve the quality of your manuscript. 

In the abstract, the research gap is not defined clearly and it is not explained why there is a need for improvement. Please briefly and clearly discuss the problem and the justification for the need for your work. It is also not clear how the evaluation of image reconstruction performed and how it improved based on the available image quality assessments. 

The introduction should be started with the problem which needs to be solved, for example, why there is a need for image reconstruction and why your method is valuable. cite related references as well.

Using of Matlab and its version should not be discussed in the method section. Moreover, the size of the image and other similar information should be discussed in the results section. Please dedicate the method section to the methodology and your methods.

line 135:  "Where,"  should not be a part of the Equation. Please include more information about equations 2-5 in the text. Start the result and discussion section with some text, not image. Please include the PSNR and SSIM, and MSE equations and include a reference for them, please refer to Table 1 of this paper: geometric feature descriptor and dissimilarity-based registration of remotely sensed imagery, for this goal.

Image are too large, make them smaller.

 

 

 

 

Author Response

Dear Reviewer 2:  Please see the attachment. Thanks for your kindness.

Author Response File: Author Response.pdf

Reviewer 3 Report

The authors present a new method (ISEE) for image reconstruction. The proposed method shows significant improvement over the method proposed before (ISED). Though the results are qualitatively and quantitatively explained well, the methodologies, explanation of equations and the notations needs significantly improvement. Apart from technical details, the manuscript must be formatted correctly before accepting for publication. Please revise and address the following issues before it can be accepted for publication.

 

Please proof read the manuscript. Significant grammer corrections, formatting need to be addressed. Generally, abstract is in present tense, where you present your novelty e.g. line 12 ISEE is (not was) also introduced ...Rewrite the abstract with precise novelty in present case and or compared to previous methods.

 

Line 28 and improved ...There are many instances of such grammer that has to be corrected.

Line 35 decomposition[1].

Line 72 - How was the observation made ? Was it outcome of the direct observation using SST (if yes, when) or by image processing

Line 85 - please quantify the clarity, what defines the clarity, any KPIs ?

Proper referencing of equations 5-8 missing

Line 184 How the conclusion based on choosing n =63 has been made is not clear

Line 196 - " one does not use
196 a hammer to tighten a bolt". It does not add value to the manuscript.

Equation 9, how does the author come to conclude this. This must be explictly mentioned.

Line 200 Table 1 (without .). Is table 1 for ISED ? but line 201 mentions about ISEE

Most of the images are too large. They can be resized.

Author Response

Dear Reviewer 3:  Please see the attachment. Thanks for your kindness.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The paper improved with the included changes 

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