基于聚类的图像稀疏去噪方法

Clustering based sparse model for image denoising

在图像去噪方法的研究中,非局部均值算法与稀疏去噪算法是近几年受到广为关注的方法。非局部均值算法将具有邻域相似性的像素点作加权平均;而稀疏去噪算法是将图像的非噪声部分用过完备字典进行稀疏表示。基于上述两种方法的思想,本文提出了基于聚类的稀疏去噪方法,该方法结合了非局部均值算法与稀疏去噪算法的优点,对相似的图像块进行聚类,并通过施加e_1/e_2范数的正则化约束,对同一类中的图像块在过完备字典上进行相同结构的稀疏表示,从而达到去噪目的。在字典的选择上,本文使用DCT字典和双正交小波字典,能够同时保留原图像中的平滑分量与细节分量。实验结果表明,本文方法比传统的稀疏去噪方法有更好的去噪效果。

Non-local means and sparse models are two important denoising methods attracting many attentions during the recent years.The non-local means method uses the weighted average of the pixels that share similar neighborhoods as the denoised result,and the sparse denoising method recovers the non-noisy components of an image by a sparse representation with a few atoms in a dictionary. Based on these two denoising methods,we propose a clustering based sparse model for image denoising,which first partitions the image patches according to their similarities,and then uses l_1/l_2 norm regularization to make the similar image patches in the same cluster share the same sparse structure when they are represented by overcompleted dictionary.For dictionary selection,two dictionaries Discrete Cosin Transformation（DCT） dictionary and bi-orthogonal wavelet dictionary are chosen to represent both smooth components and detail components of the image.Experiment results show that the proposed method has better performance of image denoising compared with some traditional sparse denoising methods.