A gradient‐type iterative method for impulse noise removal
Jinkui Liu, Haisong Cao, Yongxiang Zhao, Liqiao Zhang · Numerical Linear Algebra with Applications · 2020
Abstract Image denoising is a typical inverse problem and is hard to be solved. Fortunately, a powerful two‐phase method for restoring images corrupted by high‐level impulse noise has been proposed. The key point of the method is the computational efficiency of the second phase which requires the minimization of a smooth objective function defined on the terms of edge‐preserving potential function. In this paper, we propose an effective three‐term conjugate gradient method to restore the corrupted images in the second phase of two‐phase method. An attractive feature of the proposed method is that the search direction satisfies the sufficient descent property at each iteration without any line search. The global convergence is established for general smooth functions under the Armijo‐type line search. Preliminary numerical results are reported to indicate that the proposed method to be used for impulse noise removal is promising.