Constructive action of noise for impulsive noise removal in scalar images

2006 ◽  
Vol 42 (7) ◽  
pp. 393 ◽  
Author(s):  
A. Histace ◽  
D. Rousseau
Keyword(s):  
Impulsive Noise ◽  
Noise Removal ◽  
Constructive Action

scholarly journals Fast Switching Filter for Impulsive Noise Removal from Color Images

2007 ◽  
Vol 51 (2) ◽  
pp. 155 ◽  
Author(s):  
M. Emre Celebi ◽  
Hassan A. Kingravi ◽  
Bakhtiyar Uddin ◽  
Y. Alp Aslandogan
Keyword(s):  
Impulsive Noise ◽  
Noise Removal ◽  
Color Images ◽  
Fast Switching ◽  
Switching Filter

scholarly journals Impulsive Noise Removal by Adaptive Mathematical Morphology

2016 ◽  
Vol 112 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Marisol Mares Javier ◽  
Carlos Guillén Galván ◽  
Rafael Lemuz López
Keyword(s):  
Mathematical Morphology ◽  
Impulsive Noise ◽  
Noise Removal

ℓ 1 − αℓ 2 minimization methods for signal and image reconstruction with impulsive noise removal

2020 ◽  
Vol 36 (5) ◽  
pp. 055009 ◽  
Author(s):  
Peng Li ◽  
Wengu Chen ◽  
Huanmin Ge ◽  
Michael K Ng
Keyword(s):  
Image Reconstruction ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Minimization Methods

Adaptive-neighborhood best mean rank vector filter for impulsive noise removal

2008 ◽  
Author(s):  
Mihai Ciuc ◽  
Valeriu Vrabie ◽  
Michel Herbin ◽  
Constantin Vertan ◽  
Philippe Vautrot
Keyword(s):  
Impulsive Noise ◽  
Noise Removal ◽  
Rank Vector ◽  
Adaptive Neighborhood

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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