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although used sometimes, is too generic and non-informative. As far as I am concerned (i don't know
the opinion of other reviewers) it can be skipped. The SSIM is a better choice since it is grounded on
the human visual system. However, a table with SSIM results is not present. Please, add the table with
SSIM and a discussion section on the results.
Furthermore, although I do not request it (although it is advisable), the paper would benefit from a
larger set of test images and a statistical test (ANOVA or paired t-test) between the SSIM results of the
proposed method and the baseline methods.
With regards to the MR images, which are a good choice (the context is specific), the authors ran into
the problem of the lack of the baseline image (= image without the noise). Here, the best thing to do
would be to have an expert (phisician) provide the scores for the image quality. Again, i do not request
this, but it is a drawback in the evaluation and gives little credibility to the work presented. Since I've
seen different ways in which the CNR and SNR have been implemented, the authors should provide
more details on their implementation of the metrics.
The final issue related to the evaluation is the results. Both in the synthetic and MR images the
proposed method does not outperform any of the baseline algorithms, according to the results. In some
cases it is better in some cases not. The authors must provide a more thorough discussion on when their
approach is better than the compared ones. Furthermore, they should remove bold and generic
statements about the quality of their algorithm (e.g. "It is evident that the proposed algorithm achieves a
better edge and structure preservation than other state-of-the-art methods.") and replace them with more
objective observations.
(2) PRESENTATION issues
The introduction with related work is relatively well structured. The authors list a number of related
techniques and their drawbacks. However, when they introduce their approach it is not clear how it
differs from the related work, i.e. what is the contribution of the presented work. If the proposed work
is just another implementation of image denoising with no clear advantages over the existing
approaches, then it is not OK. Please state very clearly how your contribution differs from the others.
When you state "... both the combined denoising strategy and adaptive dimensionality reduction
approach of similar patch groups" it is not clear what the contribution is.
In Sec. 2 the authors jump too quikly to the description of the algorithm's details. Please provide a
subsection (2.1) with an overview of the procedure along with a figure showing various steps of the
workflow.
Another very important issue is the lack of domain. The authors simply state that the noise is a gaussian
noise. This is too generic. Please specify which applications (acquisition devices, processing devices ...)
add that kind of noise. Provide strong arguments for the choice of the noise model. Put your work in
context.
Last but not least, the manuscript would benefit from an improvement of english. Although the prose is