SAR image despeckling based on gradient domain convolutional sparse coding

Gao Chen; Zifeng Li; Qingfeng Zhou; Chanzi Liu

doi:10.1117/12.3023324

28 March 2024 SAR image despeckling based on gradient domain convolutional sparse coding

Gao Chen, Zifeng Li, Qingfeng Zhou, Chanzi Liu

Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 130911W (2024) https://doi.org/10.1117/12.3023324
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China

Abstract

Sparse regularization is an effective tool for synthetic aperture radar (SAR) image despeckling. Designing effective sparse regularization terms plays a very important role in this kind of method. Existing sparse regularization despeckling methods use conventional patch-based sparse representation to design regularization term. This patch-based manner will lose some important spatial information along edges between patches, resulting in staircase effect. In this paper, we propose a new Gradient domain Convolutional Sparse Coding-based (GCSC) method for SAR image despeckling, and derive a feasible algorithm to efficiently solve the corresponding nonconvex optimization problem. In contrast to the well-known sparse regularization despeckling methods that divide a SAR image into patches and process patches individually in the spatial domain or the transform domain, GCSC works on the whole SAR image to learn a convolutional sparsifying regularizer in gradient domain. By taking advantage of the gradient domain convolutional sparse coding, GCSC can capture the correlation between local neighborhoods and exploit the gradient image global correlation to produce better edges and sharp features of SAR image. Experiments conducted on real SAR images demonstrate that the proposed GCSC outperforms those state-of-the-art SAR despeckling methods in terms of subjective and objective evaluation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gao Chen, Zifeng Li, Qingfeng Zhou, and Chanzi Liu "SAR image despeckling based on gradient domain convolutional sparse coding", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 130911W (28 March 2024); https://doi.org/10.1117/12.3023324

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KEYWORDS

Synthetic aperture radar

Image compression

Image restoration

Speckle

Algorithm development

Convolution

Design

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