一种基于非局部思想的改进图像降噪算法

Image noise is a major factor to affect image visual effect. Research on the algorithm of image denoising is an important research direction in the field of digital image processing. This paper presents an improved image denoising method based on sparse and redundant representations over trained dictionaries. With the traditional image denoising algorithm based on sparse expression compared to the KSVD algorithm, this paper adds a similar block polymerization process to build the smaller and more accurate learning dictionary. The novel idea behind the proposed approach is that natural images contain so many self-similarities that signal sparsity could be further promoted. This sparsity promotion makes the learned dictionary more adaptive and accurate to restore the signal. The experimental results demonstrated that our proposed method provides better visual quality when compared to the state-of-the-art methods.