Student's-t Mixture Model Based Image Denoising Method with Gradient Fidelity Term

摘要

The mixture models based structured sparse representation (MM-SSR) method has received much attention in recent years. Especially, the student's-t mixture model based structured sparse representation (SMM-SSR) has been widely used due to the fact that it has a heavy tail and is robust to noise. In this paper, for further enhancing the performance of SMM-SSR, we attempt to incorporate the gradient fidelity term with the student's-t mixture model for image denoising. Experiment results show that our proposed method outperforms the traditional SMM-SSR method.