All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
Authors have successfully addressed the comments from the reviewers, and therefore the paper is now ready for publication.
[# PeerJ Staff Note - this decision was reviewed and approved by Rong Qu, a PeerJ Computer Science Section Editor covering this Section #]
My previous comments have been addressed.
My previous comments have been addressed.
My previous comments have been addressed.
My previous comments have been addressed.
all my comments have been addressed, i recommend accepting the paper
all my comments have been addressed, i recommend accepting the paper
all my comments have been addressed, i recommend accepting the paper
no
Although two reviewers are pleased with the updated content of the manuscript, there are also several concerns raised by one of the reviewers in accordance with the novelty of the method and the comparison with respect to the state of the art.
This manuscript proposes a SPN filter method based on the nearest pixel values
Manuscript is written in well-structured manner. All the claims are justified with the help of logical and mathematical analysis. All the results are compared with other existed models. The algorithm and flowchart of the application are added. The image datasets used in the application are appropriate and sufficient.
It is appropriate this manuscript be accepted for publication.
The algorithm used in the study is supported by mathematical equations.
Experimental study was carried out using image datasets that are widely used in the literature. In this respect, the experimental design is appropriate.
Findings were compared with state-of-the-art SPN removal methods. High success rates were obtained with the proposed method in the study.
In addition, the article was supported by statistical tests at the revision stage.
No comment.
No comment.
No comment.
No comment.
In this paper, the author proposed a nearest value based mean filter method for salt and pepper noise removal. The two steps are designed: 1-Changing the noisy pixel value with the closest pixel value or assigning their average to the noisy pixel in case there is more than one pixel with the same distance. 2-It is the updating of the calculated noisy pixel values with the average ûlter by correlating them with the noise ratio. The paper is written well and easy to follow.
The comparisons are out-dated, more new methods should be compared, also color images should be tested. In Fig 3. and 4, compared with (I), i cannot see the improvements.
The method is outdated including the referenced papers. The contribution is also limited.
Authors must take into account the suggestions made by the reviewers, especially regarding the inclusion of statistical validation for a comparison vs the state of the art, which is mandatory in this case. The paper will not be accepted otherwise.
Reviewer 2 has requested that you cite specific references. However, these citations do not appear to be relevant to your manuscript. You may add them if you believe they are relevant, but I do not expect you to include these citations, and if you do not include them, this will not influence my decision
[# PeerJ Staff Note: It is PeerJ policy that additional references suggested during the peer-review process should only be included if the authors are in agreement that they are relevant and useful #]
[# PeerJ Staff Note: Please remove the acronym for your title, for clarity. Here is a title suggestion - 'A new approach for salt and pepper noise removal: nearest value based mean filter' #]
This manuscript proposes a SPN filter method based on the nearest pixel values
Manuscript is written in well-structured manner. All the claims are justified with the help of logical and mathematical analysis. All the results are compared with other existed models. The algorithm and flowchart of the application are added. The image datasets used in the application are appropriate and sufficient.
It is appropriate this manuscript be accepted for publication.
The algorithm used in the study is supported by mathematical equations.
Experimental study was carried out using image datasets that are widely used in the literature. In this respect, the experimental design is appropriate.
Findings were compared with state-of-the-art SPN removal methods. High success rates were obtained with the proposed method in the study.
- English writing and presentation style should be improved. There contained grammatical errors, typos, or jargon.
- Quality of figures/tables should be improved significantly. For example, from Table 5 (it is figure as I think), some contents are missing, resolution must be improved.
- Lacking of the results. Currently, the authors focuses a lot on the discussions, but they should add some thing in the "Results" part.
- Statistical tests should be conducted when comparing the performance among methods/algorithms to see significant differences.
- Uncertainties of model should be reported.
- Some references related to image-based model i.e., PMID: 35648374, PMID: 34502160 should be added to attract a broader readership.
- The study is conducted on old datasets. Can they apply to some new data?
- The improvements are not much compared to previous methods.
No comment
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.