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The authors have resolved the comments adequately. The paper is accepted.
[# PeerJ Staff Note - this decision was reviewed and approved by Daniel S. Katz, a PeerJ Computer Science Section Editor covering this Section #]
The reviewers recommend the acceptance of your manuscript. For final draft, try to resolve minor issues from Reviewer 4.
1# The authors of this paper propose to enhance and denoise the original target image by Improved the Histogram Equalization Algorithm.
the manuscript is quite clear and easy to understand What the authors try to do.
In the experimental evaluation, you did not consider other algorithms. You should include the results also for at least one of those models.
For example, the authors claim that they have improved the Histogram Equalization Algorithm. They should compare Improved Histogram Equalization Algorithm and Histogram Equalization Algorithm
in conclusion, this model provides a new point of view on irregular celestial objects.
Thank you Authors for their contributions
Based on the comments given by the reviewer, The authors are advised to make "Minor Revisions" and resubmit.
- The authors have addressed most of the concerns I had with the previous draft.
- The quality of English is much better than the previous draft, and the present manuscript has a clear narrative.
- The references make it easy to follow the arguments and provides substantial background.
- Mathematical equations have been properly typeset, which was a concern for me previously.
- The research question, as stated previously, is meaningful and relevant.
- The experimental design is founded on solid prior research and addresses clearly the gaps that are not satisfied by other work. I congratulate the authors for this present manuscript.
- The analysis is based on well-founded literature on the ORB algorithm. The authors still have not included any discussion on the Hamming distance method – although this has been expressly mentioned in the abstract and in the summary. I had urged the authors to add some discussion around it.
- The arguments are solid, and I think otherwise that the research is well-founded.
- Overall, the authors have satisfied most of my concerns, and I think this is in proper shape for publication as it stands.
The authors of this paper propose to enhance and denoise the original target image by Improved the Histogram Equalization Algorithm.
the manuscript is quite clear and easy to understand.
The idea of improving the brightness and clarity of the images is interesting. The reported results show that the proposed bilateral filtering method improved histogram equalization algorithm is more accurate than before optimizing the algorithm
The flow chart and the design of the article should be done more regularly.
Improve the quality of Figure 3 Algorithm flowchart
The table 1 image column should be written more clearly.
ex :
1. row = Before HEEF Algorithm Optimization
2. row = After HEEF Algorithm
3. row = After HEEF Algorithm Optimization
Based on the reviewers' comments, the authors are requested to make "major" revisions.
[# PeerJ Staff Note: Please ensure that all review comments are addressed in a rebuttal letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. It is a common mistake to address reviewer questions in the rebuttal letter but not in the revised manuscript. If a reviewer raised a question then your readers will probably have the same question so you should ensure that the manuscript can stand alone without the rebuttal letter. Directions on how to prepare a rebuttal letter can be found at: https://peerj.com/benefits/academic-rebuttal-letters/ #]
[# PeerJ Staff Note: The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at copyediting@peerj.com for pricing (be sure to provide your manuscript number and title) #]
The review of the literature is well drafted, so the reader have adequate background about the topic.
Abstract and Introduction is c;ear and unambiguous in nature and it is well written.
The methodology used in the paper is novel and the experimental model was well explained. Sufficient details were also provided about the implementation. Originality of the implementation was carried out.
Figure, table with the data are clearly given in the paper.
Conclusion with the result are well stated, linked to the article focused.
Overall the paper is well written and it shows the future direction for carrying further research in this filed of study.
The English grammar needs to be modified. In addition, formula 1 and formula 4 show errors and need to be modified, which will affect the review.
The experimental design part is good, and efficient algorithms are presented.
The algorithm is valid.
The grammar, expressions, formulas, references, etc. of the full text still need to be improved. It is recommended to accept after minor revisions.
- The authors would need to check for grammatical and continuity issues. e.g.: in lines 46-47: the authors should clarify/re-write this sentence.
- Line 42: the authors might consider changing “my country's future” to either “our” or “China’s”
- Sufficient field background are not provided. There is a lot of literature on SIFT and ORB algorithms for edge detection. e.g., in lines 64-66: the authors might want to consider re-phrasing the findings of [11]
- Mathematical equation typesetting is hard to read: e.g., Eq (1), (2) are hard to read. Perhaps the authors can typeset on some equation editor?
- Some issues with self-containment: the authors might want to add some more background information on ORB. Also, eg., it is not clear what A, B, C and D in the matrix M of Eq (6) refer to.
- Definition and theorems: the authors might want to add a couple of lines explaining the intuition for the domain cores. Also, it will be helpful to explain why omega (Eq. 4) is a valid weighting function, i.e., does it lie between 0 and 1? In Eq (5), is there a specific reason for picking lambda = 0.5?
- Research question is meaningful and relevant.
- However, I think there are other papers which have similar ideas in them. As an example, the paper "Overview of Image Matching Based on ORB Algorithm" with DOI: 10.1088/1742-6596/1237/3/032020 has a lot of the technique in common. It is advised that the authors specify what gap the present paper fills.
- The analysis is based on well-founded literature on the ORB algorithm. However it is unclear how the Hamming distance method is utilized - the authors have mentioned it in the abstract and the summary.
Overall, the authors might need to work on rewriting some sections where they have reviewed other work. Adding in some more references is recommended.
1# The authors of this paper propose to enhance and denoise the original target image by Improved Histogram Equalization Algorithm.
2# First of all, the manuscript is quite clear and easy to understand What the authors try to do.
2#major In the experimental evaluation you did not consider other algorithms. You should include the results also for at least one of those models.
For example, the authors claim that they have improved the Histogram Equalization Algorithm. They should compare Improved Histogram Equalization Algorithm and Histogram Equalization Algorithm.
(Table 1 feature point extraction and matching data table)
3#major. The sample space is minimal and it is very difficult to generalize by looking at only one picture. They need to explain the size of the dataset. and test their improved Histogram Equalization Algorithm
on the large size of the irregular small celestial body image dataset.
4#major. Adding at least another couple of image data would extend the validity of the results.
5#minor. The flow chart and the design of the article should be done more regularly.
(Fig. 3 Algorithm flowchart)
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