Image Restoration by a Mixed High-Order Total Variation and l1 Regularization Model

الملخص EN

Total variation regularization is well-known for recovering sharp edges; however, it usually produces staircase artifacts.

In this paper, in order to overcome the shortcoming of total variation regularization, we propose a new variational model combining high-order total variation regularization and l1 regularization.

The new model has separable structure which enables us to solve the involved subproblems more efficiently.

We propose a fast alternating method by employing the fast iterative shrinkage-thresholding algorithm (FISTA) and the alternating direction method of multipliers (ADMM).

Compared with some current state-of-the-art methods, numerical experiments show that our proposed model can significantly improve the quality of restored images and obtain higher SNR and SSIM values.