L2 Regularization Model with Removal of Gaussian Noise

Abstract

In order to remove Gaussian noise from images, a Gaussian noise image restoration method based on the L2 norm regularization model was proposed. The L2 norm is selected as the data fidelity term and the gradient operator and wavelet frame as the regularization term to suppress the image ladder effect and protect the image edge details. Since the objective function of the model is a large convex function, the solving process is very tedious. The split Bregman iterative algorithm and alternate direction multiplier method are combined to restore the image. The experimental results show that show that the alternate direction multiplier method can effectively reduce the difficulty of solving the restoration model, and the image recovered by using this model has a higher peak signal-to-n ratio and better structural similarity and can get a clearer image.