A simple neuro-fuzzy method for improving the performances of impulse noise filters for digital images

2005 ◽  
Vol 59 (8) ◽  
pp. 463-472 ◽  
Author(s):  
M. Emin Yüksel
Keyword(s):  
Impulse Noise ◽  
Digital Images ◽  
Fuzzy Method ◽  
Neuro Fuzzy ◽  
Noise Filters

A Novel Approach of Restoration of Digital Images Degraded by Impulse Noise

2014 ◽  
Vol 4 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Rashmi Kumari ◽  
Anupriya Asthana ◽  
Vikas Kumar
Keyword(s):  
Impulse Noise ◽  
Fuzzy Inference ◽  
Digital Images ◽  
Window Size ◽  
Qualitative And Quantitative Analysis ◽  
Inference System ◽  
Qualitative And Quantitative ◽  
Novel Approach ◽  
Neuro Fuzzy ◽  
Restoration Method

Restoration of digital images degraded by impulse noise is still a challenge for researchers. Various methods proposed in the literature suffer from common drawbacks: such as introduction of artifacts and blurring of the images. A novel idea is proposed in this paper where presence of impulsive pixels are detected by ANFIS (Adaptive Neuro-Fuzzy Inference System) and mean of the median of suitable window size of noisy image is taken for the removal of the detected corrupted pixels. Experimental results show the effectiveness of the proposed restoration method both by qualitative and quantitative analysis.

A Novel Approach of Restoration of Digital Images Degraded by Impulse Noise

Fuzzy Systems ◽  
2017 ◽  
pp. 153-169
Author(s):  
Rashmi Kumari ◽  
Anupriya Asthana ◽  
Vikas Kumar
Keyword(s):  
Quantitative Analysis ◽  
Impulse Noise ◽  
Fuzzy Inference ◽  
Digital Images ◽  
Window Size ◽  
Noisy Image ◽  
Inference System ◽  
Novel Approach ◽  
Neuro Fuzzy ◽  
Restoration Method

Restoration of digital images degraded by impulse noise is still a challenge for researchers. Various methods proposed in the literature suffer from common drawbacks: such as introduction of artifacts and blurring of the images. A novel idea is proposed in this paper where presence of impulsive pixels are detected by ANFIS (Adaptive Neuro-Fuzzy Inference System) and mean of the median of suitable window size of noisy image is taken for the removal of the detected corrupted pixels. Experimental results show the effectiveness of the proposed restoration method both by qualitative and quantitative analysis.

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