Image Denoising Implementation Using Semi-Implicit AOS Algorithm for Regularized P-M Equation

In image processing applications, we can implement image denoising using the semi-implicit Additive Operator Splitting (AOS) algorithm for the regularized Perona-Malik equation. This algorithm proves highly effective in removing noise points and irrelevant information from images. Through iterative pixel value updates and adjustments using diffusion-based calculations, the AOS algorithm gradually enhances image clarity and recognizability. The implementation typically involves solving the regularized P-M equation through operator splitting techniques, where the diffusion process is decomposed into separate directional components for computational efficiency. This method significantly improves image quality and finds widespread application in various image processing scenarios. Therefore, when performing image denoising tasks, employing the semi-implicit AOS algorithm for the regularized P-M equation represents a highly recommended approach that balances computational stability with denoising performance.