基于稀疏表示及正则约束的图像去噪方法综述

Image Denoising Based on Sparse Representation and Regularization Constraint: A Review

数据去噪声是信号和图像处理领域的一个经典问题,广泛应用于各类工程实践中。由于噪声源的多样性,去噪一直是富有挑战性且十分活跃的研究课题,发展了多种经典去噪方法。近年来,随着压缩感知理论的发展,基于稀疏表示及正则化约束反问题求解方法成为图像去噪领域的重要发展方向和技术途径。本文首先回顾和总结图像噪声的来源和类型,然后针对不同类型的图像噪声,重点围绕基于稀疏表示及正则化约束的图像去噪技术进行全面综述,分析和描述了几种主要去噪方法的原理及优缺点。最后,对去噪算法的性能评价指标进行总结。

Data denoising is a classic issue in the field of signal and image processing which has been widely applied in various engineering practices.Due to the diversity of noise sources,denoising is a challenging and active research topic,and a variety of classical denoising methods have been developed.In recent years,with the development of compressed sensing theory,the methods for solving inverse problem based on sparse representation and regularization constraint have become important research directions and technical approaches in the field of image denoising.This paper firstly reviews and summarizes the sources and types of image noise,and then according to the different types of image noise,gives a comprehensive review focusing on the image denoising techniques based on sparse representation and regularization constraints.In addition,we analyze and describe the principle,advantages and disadvantages of several major denoising methods.Finally,the performance evaluation of denoising algorithm is summarized.