SAR image denoising based on patch ordering in nonsubsample shearlet domain

Authors: SHUAIQI LIU, QI HU, PENGFEI LI, JIE ZHAO, ZHIHUI ZHU

Abstract: Synthetic aperture radar (SAR) has been extensively adopted in a variety of fields, e.g., agriculture and marine fields. In this regard, the improvement of SAR image quality has aroused a wide concern worldwide. In recent years, image processing based on local patches has been very popular and proven feasible. In this paper, a novel SAR image denoising algorithm is proposed in the NSST domain on the basis of patch ordering. First, the shearlet transform is applied to logarithmic transformation of the noisy SAR image. Second, the coefficients of the shearlet are denoised respectively by combining patch ordering and 1D filtering. Finally, the denoised SAR image can be obtained by exponential transformation after applying the inverse shearlet to denoised coefficients. The experimental results show that the proposed method not only effectively suppresses the speckle noise and improves the PSNR and ENL of denoising the SAR images but also obviously improves the visual effects of the SAR images, especially in maintaining the image edge and texture information.

Keywords: Synthetic aperture radar image denoising, nonsubsampled shearlet transform, patch ordering

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