Image restoration with impulse noise based on fractional-order total variation and framelet transform - Parvaz, Reza (Signal, image and video processing, 2023)
Restoring images corrupted by noise and blur is a burgeoning subject in image processing, and despite the large number of proposed restoration algorithms, the effort to bring about some improvement is always of great interest. The definition of fractional derivatives in recent years has created a powerful tool for this purpose. In the present paper, using fractional-order total variation and framelet transform, the nonconvex model for image restoration with impulse noise problem is improved. Then by alternating direction method of multipliers (ADMM) and primal-dual problem, the proposed model is solved. The convergence of the proposed algorithm is studied, and the proposed algorithm is evaluated using different types of tests.