片相似性各项异性扩散图像去噪

Patch Similarity Based Anisotropic Diffusion for Image Denoising

提出了一种基于片相似性的各项异性扩散图像去噪方法.传统的各项异性图像去噪方法都是基于单个像素点的灰度相似性(或梯度信息),不能很好地保持弱梯度边缘和纹理等细节信息.基于片相似性的非局部图像去噪方法由于利用了邻域像素的灰度相似性,而能够很好地保持纹理等细节信息.将片相似性思想引入到各项异性扩散中,利用片相似性构造扩散函数,同时将片相似性各项异性扩散模型扩展到彩色图像的去噪.实验结果表明,提出的改进方法能很好地保持纹理等细节信息,不存在各项异性扩散普遍存在的明显的阶梯效应,同时比非局部图像去噪方法速度快.医学图像去噪实例也表明所提出方法具有很好的应用前景.

A patch-similarity-based anisotropic diffusion is presented for image denoising. The traditionally anisotropic diffusion based on the intensity similarity of each single pixel （or gradient information） cannot effectively preserve weak edges and details, such as texture. The non-local means algorithm based on patch similarity can preserve texture details well, since the non-local means utilizes the intensity similarity of neighbor pixels. In the proposed method, the patch similarity is adopted to construct the diffusion function of the anisotropic diffusion for gray and color image denoising, namely the diffusion coefficient of the anisotropic diffusion depends on the patch similarity, not image gradient. Therefore, the diffusion of this method is more effective and accurate than the traditional method, because image patches can represent structure information, such as edge and texture etc, while single pixel cannot represent structure information. Experimental results of gray and color images demonstrate that the proposed method can preserve details better than the traditional anisotropic diffusion, and has not noticeable staircase effect that appears in the traditional anisotropic diffusion. In addition, the time complexity is lower than that of the non-local means algorithm. Brain and cardiac magnetic resonance （MR） images and brain Chinese visible human image denoising experiments also indicate that the proposed method has a promising application.