Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing
Round 1
Reviewer 1 Report
Sparse regularization with a non-convex penalty for SAR imaging and autofocusing
General description
In this paper, SAR image reconstruction procedure is developed with phase compensation autofocusing as an inverse problem is formulated. An optimization approach based on a sparsity-enforcing Cauchy regularization is proposed. Two methods are presented capable of handling the problem’s complex nature in image reconstruction sub-problem. A complex version of the forward-backward splitting algorithm to solve the sub-problem iteratively is developed. A Wirtinger alternating minimization autofocusing method is presented. For both methods, the phase error estimation image sub-problem is solved by simply expanding and observing its cost function. The convergence of both algorithms is thoroughly discussed. Experiments are conducted on both simulated and real SAR images. The proposed method demonstrates impressive autofocusing results on these datasets compared to state of the art methods.
Remarks
The article suggests original mathematical derivations, algorithms and experimental results.
In point of view of the reviewer the general theory and main algorithms in the article need to be presented in more order fashion. The authors need to disclose the derivations of equations (3, 4, 5, 6), and the structure of vectors g, C, f. The description of the image reconstruction and autofocusing algorithm needs to follow a hierarchical order and structure.
The text needs an editing.
Author Response
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Reviewer 2 Report
The authors present a very interesting paper using, SAR image reconstruction with joint phase error estimation (autofocusing) is formulated as an inverse problem. An optimization model utilizing a sparsity-enforcing Cauchy regularizer is proposed, and an alternating minimization framework is used to solve it, in which the desired image and the phase errors are estimated alternatively. The manuscript is clear, relevant for the field and presented in a well-structured manner and scientifically sound. The manuscript’s results are reproducible based on the details given in the methods section. The manuscript is well written and should be of great interest to the readers. However, some figures with charts could be bigger (figure 5) and table 2 too. Also in the conclusion, should mention more about their future work.
Author Response
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Reviewer 3 Report
The SAR data acquisition process will effected by phase errors. These phase errors in turn result in a defocusing effect in the formed SAR images. Techniques to estimate these phase errors from the raw SAR data and to remove them are called autofocusing techniques. In this paper the SAR autofocusing problem is formed as an inverse problem, An alternating minimization framework named CFBA is proposed to solve this inverse problem. Experimental results show the method is feasible. The paper has novel idea and can be accepted.
Suggestions:
- There are a lot of methods for autofocusing technique and the paper also introduced these methods in the section introduction. The paper should explain what are the shortcomings of there existed methods and why need a new method. In other words, what is the advantages of the new method. why does need this new method.
- The content from line76 in page2 to line 114 in page 3 can be compressed and some content here can be moved to the section 5 or section 6
Author Response
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