An efficient nonlinear approach for removing fixed-value impulse noise from grayscale images
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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.
KeywordsSalt and pepper noise Noise suppression Nonlinear approach Grayscale images GPU
The authors are grateful with the editor and with the reviewers for their valuable comments and insightful suggestions, which can help to improve this research significantly. The authors thank to CONACYT as well as Tecnológico Nacional de México (TecNM)/Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET) for their financial support through the project 5688.16-P named "Sistema para procesamiento de imágenes de resonancia magnética para segmentación 3D y visualización de tejidos cerebrales".
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