Image denoising via a new hybrid TGV model based on Shannon interpolation

Abstract

A new hybrid variational model is presented for image denoising, which in-corporates the merits of Shannon interpolation, total generalized variation (TGV) regularization, and a symmetrized derivative regularization term based on l1-norm. In this model, the regularization term is a combination of a TGV functional and the symmetrized derivative regularization term, while the data fidelity term is characterized by the l2-norm. Unlike most variational models that are discretized using a finite-difference scheme, our approach in structure is based on Shannon interpolation. Quantitative and qualitative assessments of the new model indicate its effectiveness in restoration accuracy and staircase effect suppression. Numerical experi-ments are carried out using the primal-dual algorithm. Numerous real- world examples are conducted to confirm that the newly proposed method outperforms several current state-of-the-art numerical methods in terms of the peak signal to noise ratio and the structural similarity (SSIM) index.

Keywords

• variational model
• total generalized variation regularization
• staircasing effect
• primal-dual algorithm