Image Denoising Using Total Variation Method

This program utilizes the total variation method for image denoising. The total variation approach is a widely-used image denoising technique that eliminates noise by minimizing the variations between adjacent pixels in an image. This method employs a regularization term that preserves edges while smoothing homogeneous regions, typically implemented through gradient descent or primal-dual optimization algorithms. In practical implementation, the algorithm often involves solving a partial differential equation where the total variation norm serves as the regularization constraint, effectively balancing noise removal and edge preservation. Widely applied in image processing domains, this method has been proven as an effective denoising solution through both theoretical analysis and experimental validation. We hope users will find value and satisfaction in utilizing this program, which includes configurable parameters for regularization strength and iteration controls to adapt to various noise levels and image types.