A parameter-adaptive iterative regularization model for image denoising

In this article, an iterative regularization model (IRM) with adaptive parameter is addressed. IRM has gained a lot of attentions. But constant scale parameter becomes very sensitive for the fast convergence. It becomes very important to optimize the scale parameter adaptively. Therefore, we introduce a novel IRM with varying scale parameter because of the fact that when the scale parameter is smaller, the number of the iteration will enhance by IRM. A method to estimate a scale parameter is proposed according to the trend of the scale parameter. And the theoretical justification for this approach can be inferred. Numerical experiments show that the proposed methods with varying scale parameter can efficiently remove noise, reduce the number of iteration, and well preserve the details of images.