TV-based Impulsive Noise Reduction with Weber Law Detector

2019 ◽  
Vol 63 (5) ◽  
pp. 50405-1-50405-10 ◽  
Author(s):  
Heri Prasetyo ◽  
Chih-Hsien Hsia ◽  
Kun-Yi Yu
Keyword(s):  
Total Variation ◽  
Noise Suppression ◽  
Impulsive Noise ◽  
Objective Assessment ◽  
Noise Removal ◽  
Noise Detection ◽  
Impulsive Noise Suppression ◽  
A New Technique ◽  
Impulsive Noise Reduction ◽  
Noise Map

Abstract This article proposes a new technique for impulsive noise removal. This technique consists of two steps: (1) impulsive noise detection, and (2) impulsive noise suppression. The proposed method exploits the effectiveness of Weber Law in detecting and locating the impulsive noise appearing in the corrupted image. The occurrence of impulsive noise is then reduced and suppressed using the Total Variation-based approach with the detected noise map obtained from the Weber Law detector. As documented in the Experimental section, the proposed method offers promising results in terms of visual investigation. In addition, it also gives superior results compared to that of the former competing schemes under objective assessment. Thus, it can be regarded as a good candidate for impulsive noise removal algorithm.

Impulsive noise suppression and analysis in color imaging

2004 ◽  
Author(s):  
Volodymyr I. Ponomaryov ◽  
Alberto J. Rosales ◽  
Francisco Gallegos Funes ◽  
Francisco Gomeztagle
Keyword(s):  
Noise Suppression ◽  
Impulsive Noise ◽  
Color Imaging ◽  
Impulsive Noise Suppression

scholarly journals Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2782
Author(s):  
Krystian Radlak ◽  
Lukasz Malinski ◽  
Bogdan Smolka
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Network Architecture ◽  
Noise Suppression ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Vision Systems ◽  
Mean Filter ◽  
Computer Vision Systems ◽  
Active Research

Noise reduction is one of the most important and still active research topics in low-level image processing due to its high impact on object detection and scene understanding for computer vision systems. Recently, we observed a substantially increased interest in the application of deep learning algorithms. Many computer vision systems use them, due to their impressive capability of feature extraction and classification. While these methods have also been successfully applied in image denoising, significantly improving its performance, most of the proposed approaches were designed for Gaussian noise suppression. In this paper, we present a switching filtering technique intended for impulsive noise removal using deep learning. In the proposed method, the distorted pixels are detected using a deep neural network architecture and restored with the fast adaptive mean filter. The performed experiments show that the proposed approach is superior to the state-of-the-art filters designed for impulsive noise removal in color digital images.

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