Image fusion using Bi-dimensional Empirical Mode Decomposition for salt and pepper noise removal in digital images

2012 ◽  
Author(s):  
M. Prema Kumar ◽  
P. Rajesh Kumar
Keyword(s):  
Image Fusion ◽  
Empirical Mode Decomposition ◽  
Digital Images ◽  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Mode Decomposition ◽  
Salt And Pepper

scholarly journals A “Salt and Pepper” Noise Reduction Scheme for Digital Images Based on Support Vector Machines Classification and Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Hilario Gómez-Moreno ◽  
Pedro Gil-Jiménez ◽  
Sergio Lafuente-Arroyo ◽  
Roberto López-Sastre ◽  
Saturnino Maldonado-Bascón
Keyword(s):  
Support Vector Machines ◽  
Noise Reduction ◽  
Digital Images ◽  
State Of The Art ◽  
Noise Removal ◽  
Support Vector ◽  
Salt And Pepper Noise ◽  
Vector Machines ◽  
Classification And Regression ◽  
Salt And Pepper

We present a new impulse noise removal technique based on Support Vector Machines (SVM). Both classification and regression were used to reduce the “salt and pepper” noise found in digital images. Classification enables identification of noisy pixels, while regression provides a means to determine reconstruction values. The training vectors necessary for the SVM were generated synthetically in order to maintain control over quality and complexity. A modified median filter based on a previous noise detection stage and a regression-based filter are presented and compared to other well-known state-of-the-art noise reduction algorithms. The results show that the filters proposed achieved good results, outperforming other state-of-the-art algorithms for low and medium noise ratios, and were comparable for very highly corrupted images.

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