Fractional-Order Anisotropic Diffusion Image Denoising

We have successfully achieved image denoising objectives using the fractional-order anisotropic diffusion algorithm. This approach is fundamentally based on the methodology presented in the paper "Fractional-Order Anisotropic Diffusion for Image Denoising" by Jian Bai and Xiang Chu, published in 2007 in IEEE T. Image Process. The implementation involves solving fractional-order partial differential equations that adaptively smooth homogeneous regions while preserving edges through anisotropic diffusion coefficients. The core algorithm utilizes gradient magnitude calculations to control diffusion strength, where lower diffusion occurs near edges to maintain structural integrity. Through fractional-order anisotropic diffusion processing, we effectively reduce image noise while significantly enhancing image quality. This method demonstrates broad application prospects in image processing field, capable of improving visual effects, enhancing image details, and increasing image clarity. The MATLAB implementation typically involves iterative updates of pixel values using discretized fractional derivatives and adaptive time stepping for stability.