Paper Info
Title
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.
Year
DOI
Venue
2018
10.1007/s11554-017-0746-8
J. Real-Time Image Processing
Keywords
Field
DocType
Salt and pepper noise,Noise suppression,Nonlinear approach,Grayscale images,GPU
Noise reduction,k-nearest neighbors algorithm,Computer vision,Sliding window protocol,Nonlinear system,Computer science,Salt-and-pepper noise,Impulse noise,Artificial intelligence,Pixel,Grayscale
Journal
Volume
Issue
ISSN
14
3
1861-8200
Citations 
PageRank 
References 
3
0.40
18
Authors
4
Name
Order
Citations
PageRank
Dante Mújica-Vargas1102.55
José De Jesús Rubio257436.29
Jean Marie Vianney Kinani352.45
Francisco J. Gallegos Funes46310.29