Impulse noise removal based on new hybrid conjugate gradient approach
Morteza Kimiaei, Majid Rostami · Kybernetika · 2016
summary:Image denoising is a fundamental problem in image processing operations. In this paper, we present a two-phase scheme for the impulse noise removal. In the first phase, noise candidates are identified by the adaptive median filter (AMF) for salt-and-pepper noise. In the second phase, a new hybrid conjugate gradient method is used to minimize an edge-preserving regularization functional. The second phase of our algorithm inherits advantages of both Dai-Yuan (DY) and Hager-Zhang (HZ) conjugate gradient methods to produce the new direction. The descent property of new direction in each iteration and the global convergence results are established under some standard assumptions. Furthermore, we investigate some conjugate gradient algorithms and the complexity analysis of theirs. Numerical experiments are given to illustrate the efficiency of the new hybrid conjugate gradient (HCGN) method for impulse noise removal.