Improvement of conjugate gradient methods for removing impulse noise images

Abstract

Optimization problems occur in most disciplines like engineering, physics, mathematics, economics, administration, commerce, social sciences, and even politics. The conjugate coefficient is the cornerstone of conjugate gradient algorithms with the desired conjugate property. In this study, we discovered fresh second order information for the Hessian from the target function, which might lead to a new search direction. Based on a unique search direction, we proposed the update formula and nonlinear conjugate gradient technique. Under Wolfe line search and moderate objective function assumptions, the strategy has acceptable descent property and is always globally convergent. According to numerical results, the technique is successful and competitive in recovering the original picture from an image corrupted by impulsive noise.