Impulse noise removal by an adaptive trust-region method

Abstract

This paper suggests a two-phase scheme for the impulse noise removal. In the first phase, an adaptive median filter (AMF) identifies noise candidates created by salt-and-pepper method. In the second phase, a new trust-region phase recovers noise pixels detected by the first phase. The trust-region phase produces a new adaptive radius strategy using the projected gradient for the cases where iterations are successful and a low-memory nonmonotone spectral projected gradient method (SPG) where iterations are unsuccessful. We solve the trust-region subproblem without bound constraints by the dogleg strategy (DG) using the structure of the compact limited memory Broyden–Fletcher–Goldfarb–Shanno technique. The solution is then projected into the boundary region. Numerical results are given to illustrate the efficiency of the new approach for the impulse noise removal.