TV/L2-based image denoising algorithm with automatic parameter selection

In order to improve the adaptiveness of TV/L2-based image denoising algorithm in different signal-to-noise ratio (SNR) environments, an iterative denoising method with automatic parameter selection is proposed. Based upon the close connection between optimization function of denoising problem and regularization parameter, an updating model is built to select the regularized parameter. Both the parameter and the objective function are dynamically updated in alternating minimization iterations, consequently, it can make the algorithm work in different SNR environments. Meanwhile, a strategy for choosing the initial regularization parameter is presented. Considering Morozov discrepancy principle, a convex function with respect to the regularization parameter is modeled. Via the optimization method, it is easy and fast to find the convergence value of parameter, which is suitable for the iterative image denoising algorithm. Comparing with several state-of-the-art algorithms, many experiments confirm that the denoising algorithm with the proposed parameter selection is highly effective to evaluate peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), especially in low SNR environment.