Systems Engineering and Electronics ›› 2020, Vol. 42 ›› Issue (4): 813-818.doi: 10.3969/j.issn.1001-506X.2020.04.11

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SAR image despeckling based on non-convex weighted norm constraint

Shujun LIU(), Jian SONG(), Xiaodong SHEN(), Jianxin CAO()   

  1. College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
  • Received:2019-03-27 Online:2020-03-28 Published:2020-03-28
  • Supported by:
    国家自然科学基金(61701055);重庆市基础研究与前沿探索(cstc2018jcyjAX0161);重庆市基础研究与前沿探索(cstc2016jcyjA0134)

Abstract:

The non-convex weighted norm constrained (NWNC) synthetic aperture radar (SAR) image despeckling method is proposed based on the low rank property of similar image patch sets. Firstly, some similar image patches are searched for each target patch to construct a set of similar image patches. Secondly, NWNC is applied to the coefficient matrix. Then the coefficient matrix is estimated by generalized threshold shrinkage. Finally, the despeckled image is reconstructed by the inverse transformation of the coefficient matrix. The experimental results show that NWNC not only effectively solves the problem of insufficient constraints of traditional low-rank kernel norms, but also makes the coefficients estimation more accurate through NWNC and generalized threshold shrinkage, which can suppress speckle noise and protect the texture details of the image well.

Key words: synthetic aperture radar (SAR) image despeckling, low-rank, non-convex weighted norm constrain (NWNC), generalized threshold shrinkage

CLC Number: 

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