Bayesian Shearlet shrinkage for SAR image de-noising via sparse representation
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As SAR has been widely used nearly in every field, how to improve SAR’s image in both quality and visual effect has become necessary. Before what we really process the SAR image like image segmentation, edge detection, target detection or other processing, we must suppress the speckle noise in the image firstly. By analyzing the sorts and origins of noises, we present a new de-noising method of SAR image in the Shearlet domain based on sparse representation and Bayesian theory. Firstly, we apply the Shearlet transform to the noised SAR image. Secondly, we construct a new de-noising model via sparse representation and then use iterative algorithm based on Bayesian theory to solve it. Lastly, we can obtain the clean SAR image from the de-nosing Shearlet coefficients. The experimental results show that the proposed algorithm can not only effectively suppress speckle noise to improve the PSNR of SAR image, but also significantly improves the visual effect of SAR image, especially in enhancing the image’s texture.
KeywordsSAR image de-nosing Sparse representation Shearlet de-nosing Conjugate gradient method
This project is supported by the National Natural Science Foundation of China: (No. 60572093), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20050004016), Aviation Science Foundation (No. 201120M5007).
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