An efficient nonlinear approach for removing fixed-value impulse noise from grayscale images

Abstract

Removal of salt and pepper noise has been one of the most interesting researches in the field of image preprocessing tasks; it has two simultaneous stringent demands: the suppression of impulses and the preservation of fine details. To address this problem, a scheme based on nonlinear filters is proposed; it consists of the introduction of a redescending M-estimator within the modified nearest neighbor filter. In order to analyze all pixels in the neighborhood, as well as to reduce the magnitude of the existing impulses, a redescending M-estimator is used; the remaining pixels are processed by the modified nearest neighbor filter to obtain the best estimation of a noise-free pixel. The impulsive suppression is applied on the entire image by using a sliding window; the local information obtained by this one also allows to calculate the thresholds that characterize the influence function tested in the redescending M-estimator. To suppress high density fixed-value impulse noise in large-size grayscale images, the proposal is implemented on a heterogeneous CPU–GPU architecture. The noise reduction and the processing time of the proposed approach are evaluated by extensive simulations; its effectiveness is verified by quantitative and qualitative results.