Speckle Suppression Based on Sparse Representation with Non-Local Priors

Shuaiqi Liu,Chong Wang,Qi Hu,Pengfei Li,Zhihui Zhu,Jie Zhao

doi:10.3390/rs10030439

Abstract

As speckle seriously restricts the applications of remote sensing images in many fields, the ability to efficiently and effectively suppress speckle in a coherent imaging system is indispensable. In order to overcome the over-smoothing problem caused by the speckle suppression algorithm based on classical sparse representation, we propose a non-local speckle suppression algorithm that combines the non-local prior knowledge of the image into the sparse representation. The proposed algorithm first applies shearlet to sparsely represent the input image. We then incorporate the non-local priors as constraints into the image sparse representation de-noising problem. The denoised image is obtained by utilizing an alternating minimization algorithm to solve the corresponding constrained de-noising problem. The experimental results show that the proposed algorithm can not only significantly remove speckle noise, but also improve the visual effect and retain the texture information of the image better.

Highlights

The ability to efficiently and effectively suppress speckle is indispensable, since speckle noise is generally serious
The de-noising algorithms were, respectively: Lee filter [4], the Bayesian threshold shrinkage de-noising algorithm in shearlet domain based on sparse representation in [22] (BSS-SR), the nonlocal synthetic aperture radar (SAR) image denoising algorithm based on local linear minimum-mean-square-error (LMMSE) wavelet shrinkage in [24] (SAR-block method of 3-dimension (BM3D)), the Bayesian shrinkage de-noising algorithm in shearlet domain based on continuous cycle spinning in [33] (CS-BSR), the blind de-noising algorithm based on weighted nuclear norm in [34] (BWNNM), and the speckle suppression algorithm based on sparse domain with non-local priors (NL-SR) proposed in this paper
This paper first reviewed the history of speckle suppression and the framework of de-noising based on a sparse domain

Summary

Introduction

The ability to efficiently and effectively suppress speckle is indispensable, since speckle noise is generally serious. This method has a high signal-to-noise ratio, and has a good visual effect Both the shape of the block and the joint filtering algorithm can be improved, such as the method in [28] which uses a polygon as the block shape and uses principal component analysis (PCA) to reduce the dimension to find the optimal sparse block. The non-local prior information of an image is applied to the de-noising model based on sparse representation, and we can obtain a new speckle suppression algorithm in a sparse domain with non-local priors. In order to overcome the disadvantages of the de-noising model based on sparse representation in shearlet domain, we propose a new speckle suppression model based on sparse representation combined with non-local image. Structure, and we transform the noise suppression into an optimization problem and propose an algorithm to solve it

Shearlet Transform

Non-Local De-Noising

The Speckle Suppression Model

The Alternating Algorithm for Speckle Suppression

Experimental Results and Analysis

Conclusions