Clustering-based SAR image denoising by sparse representation with KSVD

• Published in: IEEE International Geoscience and Remote Sensing Symposium proceedings pp. 5003 - 5006
• Main Authors: Yunshu Zhang, Kefeng Ji, Zhipeng Deng, Shilin Zhou, Huanxin Zou
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2016
• Subjects: cluster; Clustering algorithms; Dictionaries; Encoding; Filtering algorithms; Image denoising; non-local; SAR image denoising; sparse representation; Speckle; Synthetic aperture radar
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS.2016.7730305

Speckle existed in SAR image is an undesirable product of specific imaging principle which influences SAR image interpretation and processing. In this paper, a new SAR image denoising algorithm has been proposed combining cluster with sparse representation under the non-local methodology. Due to the similar clustered patches, the sparsity coding of clustered patches is sparser. And clustered patches with similar structure could have the same constraint condition defined by the center of clustering. Thus, the non-local patches are clustered and filtered as a whole with shrinked sparsity coding. This algorithm has preferable denoising results on both simulated images and real SAR images. Experiments show prospects with speckle of different degrees compared with state-of-the-art despeckling methods. Proposed algorithm performs well both in noise reduction and detail preservation.