Image Restoration Using Fixed-Point-Like Methods for New TVL1 Variational Problems

Abstract

In this paper, we first propose two TVL1 variational problems for restoring images degraded by blurring and impulse noise, and then we propose two fixed-point-like methods, using proximal operators, for solving the new proposed TVL1 problems. Numerical experiments for several test images blurred by Gaussian kernel and corrupted by salt-and-pepper impulse noise are provided to demonstrate the efficiency and reliability of the proposed fixed-point-like methods. Numerical results show that two fixed-point-like methods for solving the new TVL1 variational problems perform very well in both PSNR (Peak signal-to-noise ratio) values and CPU time as compared with the fixed-point-like methods for solving two existing TVL1 variational problems.