Contourlet domain SAR image de-speckling via self-snake diffusion and sparse representation

Abstract

In this paper, A contourlet domain SAR image de-speckling algorithm via self-snake diffusion and sparse representation theory is presented in order to reduce the influence of the SAR image speckle noise on the large-scale target edge information of the low frequency subband and the texture information of the high frequency subband. For this algorithm, firstly, the contourlet transform is applied to the speckled SAR image, adjusts the directional number of each dimension to represent SAR image in the high dimensional space. Then, the low frequency subband without sparsity is filtered by self-snake diffusion and the filtered coefficient is regarded as the local average estimate of the low-frequency subband in the contourlet domain. Sparse representation optimization model of SAR image is presented for suppressing the speckle noise of the high frequency subbands with sparsity, and solves sparse coefficients of the high frequency subbands by using the improved orthogonal matching pursuit algorithm. Finally, the de-speckled image is reconstructed from all of the filtered subband coefficients by the inverse contourlet transform. This paper simulates three representative experiments and the experimental results demonstrate that the proposed algorithm has a better de-speckling performance with preserving the edge of the SAR image.