Image Restoration for Noise-Contaminated Images Using Total Variation Model

Restoring noise-contaminated images using the Total Variation (TV) model. The Total Variation model is a widely used image restoration method that recovers damaged images by minimizing pixel value variations across the image. This approach enhances image quality and clarity by smoothing edges while effectively reducing noise impact. The TV model employs gradient descent optimization to minimize the TV norm functional, typically implemented through iterative algorithms like the Chambolle algorithm or primal-dual methods. In practical implementation, the algorithm balances data fidelity terms and regularization terms using parameters like lambda to control denoising intensity. The Total Variation model has extensive applications in image processing, including denoising, image enhancement, and edge detection. Therefore, utilizing the TV model for restoring noise-polluted images represents an effective and commonly adopted approach in computational photography and computer vision applications. Key functions in implementation typically involve gradient computation, divergence operators, and iterative update schemes that preserve edges while removing noise artifacts.