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Article
Peer-Review Record

A Multi-Spectral Temperature Field Reconstruction Technology under a Sparse Projection

Photonics 2024, 11(8), 767; https://doi.org/10.3390/photonics11080767
by Xuan Zhang 1 and Yan Han 2,3,*
Reviewer 2: Anonymous
Reviewer 3:
Photonics 2024, 11(8), 767; https://doi.org/10.3390/photonics11080767
Submission received: 7 July 2024 / Revised: 12 August 2024 / Accepted: 15 August 2024 / Published: 16 August 2024

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This work deals with a very important problem of the tomographic image reconstruction in the optical emission with limited projected data. For the usual solution of this inversion problem, a complete set of projective data covering the full angular region is required. In the current paper, more information is added by using the multi-spectral temperature technique while only two projections are used.

I have some reservations about the general applicability and correctness of the presented method, as a temperature field reconstruction technique, to be widely used as a reconstruction method under sparse projection information. Ι would therefore like the have following questions answered:

General Questions

(1)   Ιs it absolutely necessary for the two projection acquisitions to be orthogonal to each other? What will happen if this condition is not fulfilled? For example, could this technique work for two projections that have an angular distance of 60 degrees?

(2)   How is the reconstruction result correlated with the symmetry of the depicted field? In the current paper only one or four peaks with an exact location and size symmetry are presented. How a more complicated and asymmetric phantom will be reconstructed, especially in the case of a large object which shadows a smaller one and it turns out impossible to resolve their exact location and size from the two orthogonal projections?

(3)   How the introduced number of the field sub-regions will affect the measured resolution? For the reported errors, is an improvement expected in the reconstruction results for the single- and multi-peak RMS error if a larger partition is used?

(4)   Is the spectral-line information added in the presented technique sufficient to recover the missing spatial information and how?  Is the final image not a simple superposition of each spectral reconstruction?

Specific Questions and Remarks

(1)   The physical meaning of the equation (6) is not clear. How is the temperature reconstructed? What is the underlying physical law? What are the measured quantities in these equations? What are the coefficients eta (η_i) and epsilon (ε_i), the constant C_2 and the function Δf?

(2)   The Neural Network used for optimization purposes, as it appears in Figure 4€, needs more explanation. Similarly, the T functional as shown in the sub-process (f) of the same figure (related to the previous question).

(3)   The total number of detection points in each projection and the number of spectral lines must be clearly given. This will facilitate the reading and understanding of Table 2.    

Finally, the manuscript must be further checked to remove any linguistic or syntactical mistakes and slips.

Comments on the Quality of English Language

The manuscript must be further checked to remove any linguistic or syntactical mistakes and slips.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript proposed a multi-spectral temperature field reconstruction technology under a sparse projection. It can reconstruct the temperature field under two projection angles without prior knowledge. This research has some potential for application, but it still needs some improvement. Here are some of my suggestions:

(1)   In 3.1&3.2, is it possible to illustrate the precision of the reconstruction by just two examples? The error of Single-peak and Multi-peak in Table 3 is quite different. Does the obtained range of 1.64%-12.25% cover most cases?

(2)  In the Experimental test (should this be 3.3? Line 268), what was the purpose of the experiment intended to illustrate? Was the multi-spectral temperature field reconstruction technology mentioned in the manuscript used? How was the multi-spectral and temperature field information obtained, and on what basis were the reconstructed results of Fig.7.(b)(d)(f)? How is its quantitative accuracy?

(3)   Are there any requirements for the two angles from which the temperature field reconstruction is performed? Any two?

(4)   The results in Table 4 hardly seem to suggest that the proposed method is more robust.

 

(5)   I noticed a high match ratio in this manuscript (30%), especially in the Introduction section. Authors should summarize existing research and analyze their shortcomings in their own words, e.g. Lines 81-92, without having to describe others' methods in detail.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript developed a multi-spectral temperature field reconstruction technology combined with equation constrained optimization algorithm to establish rules for missing information. Therefore, the temperature field can be reconstructed under two projection angles without any prior knowledge. There are some problems:

1.  in the introduction, the manuscript detailed introduced the developments in the field of tomography in the case of ordinary and limited projection views, however, it is best to provide key parameters for comparison. 

2. in the part of basic principles, the corresponding angles in equation (1) should be labelled in the figure 1 and introduced in the text. 

3. in the part of 2.1 section, the authors said "multi-spectral temperature measurement method based on optimization ideas can get rid of the dependence of measurement technology on material spectral characteristics. ", what is the basis for this conclusion and how was it obtained? 

4. How is equation (6) obtained? The parameters in equation (6) need to be explained in the text.

5. It doesn't seem reasonable if ignoring the red part in the figure 2.

6. The manuscript employed many results of reference [15], which should be introduced in this manuscript in the convenience of readers.

7. Figure 4(b) does not match the description in the manuscript. In addition, the captions of sub figures in figure 4 should be added.

8. In figure 4(c), how is the inlet equation is obtained and what is the meaning?

9. How is equation 10 is obtained and what is the meaning?

10. In conclusion, the authors have not clearly described the main principles and the existing arguments are not enough to support the conclusion. 

Comments on the Quality of English Language

English should be polished.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I would like to thank the authors for carefully analyzing my all my posed questions / remarks and having corrected and improved the text accordingly. I believe that the manuscript now reached the journal's quality standards and can be accepted for publication.

Author Response

Thank you very much for your review of our manuscript and your recognition of our research.

Reviewer 2 Report

Comments and Suggestions for Authors

The authors responded to the reviewers‘ comments in great detail and made corresponding changes to the manuscript. I believe that this manuscript can be accepted for publication.

Author Response

Thank you very much for your review of our manuscript and your recognition of our research.

Reviewer 3 Report

Comments and Suggestions for Authors

The new manuscript has been basically revised according to the review comments, but there are also some minor issues as below.

1. There is still room for improvement in the introduction section. 

2. In line 367, how to ensure the symmetry of the temperature field? Are symmetry assumptions used in experiments and reconstructions?

3. In figure 10, why the error decreases at first and then increases with the number of sub-regions? Why the error is minimum when the number of sub-regions is around 49?   The analysis should be added in the text.

4. Suggest adding references about the neural network algorithm used in this manuscript.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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