CAJ | 학술논문

该文提出一种具有图像结构自适应性的非局部均值去噪算法。通过采用图像块间的多尺度匹配来描述图像中局部结构的相似性，增强非局部均值算法对复杂结构特征的辨别能力，实现去噪性能的提高。算法首先引入变差系数(CV)特征并提出 CV-Kmeans 区域分类算法，将图像划分为包含边缘及纹理的结构区域和平坦区域；在结构区域进一步根据不同尺度下图像块间的平均欧氏距离来自适应选择块尺寸；在此基础上获得新的非局部均值算法，用以去除图像噪声。实验结果表明，相比经典的非局部均值算法，基于块间概率相似性的改进型非局部均值算法以及基于区域自适应的非局部均值去噪算法提出的新算法提高了去噪性能，尤其是在纹理图像的去噪上具有明显优势。
해문제출일충구유도상결구자괄응성적비국부균치거조산법。통과채용도상괴간적다척도필배래묘술도상중국부결구적상사성，증강비국부균치산법대복잡결구특정적변별능력，실현거조성능적제고。산법수선인입변차계수(CV)특정병제출 CV-Kmeans 구역분류산법，장도상화분위포함변연급문리적결구구역화평탄구역；재결구구역진일보근거불동척도하도상괴간적평균구씨거리래자괄응선택괴척촌；재차기출상획득신적비국부균치산법，용이거제도상조성。실험결과표명，상비경전적비국부균치산법，기우괴간개솔상사성적개진형비국부균치산법이급기우구역자괄응적비국부균치거조산법제출적신산법제고료거조성능，우기시재문리도상적거조상구유명현우세。
A distinct non-local means denoising algorithm derived from structure-adapted block matching is proposed in this paper. Multi-scale matching of image blocks is adopted to measure similarity of local structures, which can deal with complex structural characteristics effectively and subsequently improve denoising performance. To begin with, structural region (including edges and textures) and flat region are divided by introducing Coefficient of Variation (CV) characteristics and the CV-Kmeans region classification algorithm is proposed. Furthermore, the size of similar block is adaptively selected based on average Euclidean distance between blocks in structural regions. Finally, a new non-local means algorithm is proposed to remove noise. Compared to the classical non-local means algorithm, the improved algorithm using patch probabilistic similarity and the adapted non-local means denoising algorithm, experimental results show that the proposed algorithm increases denoising performance and especially demonstrates a distinct advantage in texture images.