Sar Image Autofocusing Using Wirtinger Calculus and Cauchy Regularization

Zhang, Zi-Yao; Pappas, Odysseas; Achim, Alin

doi:10.1109/icassp39728.2021.9413629

Cited by 2 publications

(4 citation statements)

References 22 publications

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“…The second method is the sparsity-driven autofocus (SDA) method of [12] (we choose the approximate l 1 norm as the regularizer of their cost function as an example), a state-of-the-art SAR autofocusing technique operating in an inverse problem framework similar to the one proposed in this paper. The remaining two methods are the WAMA method in [27] and the proposed CFBA method.…”

Section: Resultsmentioning

confidence: 99%

“…In this section, we review the Wirtinger alternating minimization autofocusing (WAMA) method originaly proposed in [27]. After that, we briefly expand on how to extend this method to several other cost functions with the same fidelity term but with different regularizers.…”

Section: The Original Methodsmentioning

confidence: 99%

“…Consequently, we refer to it as "magnitude Cauchy regularization". Like the l p norm, it too is a regularization term enforcing statistical sparsity [32], whose effectiveness has already been validated in SAR autofocusing [27], as well as SAR imaging and other inverse problems [33,34].…”

Section: The Optimization Modelmentioning

confidence: 99%

“…The second method is based on Wirtinger calculus [23][24][25][26] which is designed to deal with differentiable (in the sense of [23]) real-valued functions with complex variables. The second method has been introduced in our previous work [27], referred to as the Wirtinger alternating minimization autofocusing (WAMA) method, but we further give a thorough discussion of its convergence and show that it can be extended to the cases of several other regularizers.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing

et al. 2022

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In this paper, SAR image reconstruction with joint phase error estimation (autofocusing) is formulated as an inverse problem. An optimization model utilising 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. For the image reconstruction sub-problem (f-sub-problem), two methods are presented that are capable of handling the problem’s complex nature. Firstly, we design a complex version of the forward-backward splitting algorithm to solve the f-sub-problem iteratively, leading to a complex forward-backward autofocusing method (CFBA). For the second variant, techniques of Wirtinger calculus are utilized to minimize the cost function involving complex variables in the f-sub-problem in a direct fashion, leading to Wirtinger alternating minimization autofocusing (WAMA) method. For both methods, the phase error estimation sub-problem is solved by simply expanding and observing its cost function. Moreover, the convergence of both algorithms is discussed in detail. Experiments are conducted on both simulated and real SAR images. In addition to the synthetic scene employed, the other SAR images focus on the sea surface, with two being real images with ship targets, and another two being simulations of the sea surface (one of them containing ship wakes). The proposed method is demonstrated to give impressive autofocusing results on these datasets compared to state-of-the-art methods.

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Section: Resultsmentioning

confidence: 99%

Section: The Original Methodsmentioning

confidence: 99%