基于变指数的片相似性扩散图像降噪算法

针对图像去噪过程中存在边缘保持与噪声抑制之间的矛盾，提出了一种基于变指数的片相似性扩散图像降噪算法。算法基于变指数的自适应降噪模型，引入片相似性的思想，构造出新的边缘检测算子和扩散系数函数。传统的各项异性扩散图像降噪算法利用单个像素点的灰度相似性（或梯度信息）检测边缘，不能很好地保持图像的弱边缘和纹理信息。而所提算法利用邻域像素的灰度相似性，可以在滤除图像噪声的同时，保持更多的细节信息。仿真结果表明，与其他传统的基于偏微分方程（PDE）的图像降噪算法相比，该算法将信噪比（SNR）和峰值信噪比（PSNR）提高至16.602480dB和31.284672dB，具有良好的抗噪性；同时视觉效果较好，保持了更多的弱边缘和纹理等细节特征，在噪声抑制与边缘保持之间取得了较好的权衡。

Patch similarity anisotropic diffusion algorithm based on variable exponent for image denoising

Concerning the contradiction between edge-preserving and noise-suppressing in the process of image denoising, a patch similarity anisotropic diffusion algorithm based on variable exponent for image denoising was proposed. The algorithm combined adaptive Perona-Malik (PM) model based on variable exponent for image denoising and the idea of patch similarity, constructed a new edge indicator and a new diffusion coefficient function. The traditional anisotropic diffusion algorithms for image denoising based on the intensity similarity of each single pixel (or gradient information) to detect edge cannot effectively preserve weak edges and details such as texture. However, the proposed algorithm can preserve more detail information while removing the noise, since the algorithm utilizes the intensity similarity of neighbor pixels. The simulation results show that, compared with the traditional image denoising algorithms based on Partial Differential Equation (PDE), the proposed algorithm improves Signal-to-Noise ratio (SNR) and Peak-Signal-to-Noise Ratio (PSNR) to 16.602480dB and 31.284672dB respectively, and enhances anti-noise capability. At the same time, the filtered image preserves more detail features such as weak edges and textures and has good visual effects. Therefore, the algorithm achieves a good balance between noise reduction and edge maintenance.