Median filter based on fuzzy rules and its application to image restoration
Fuzzy Sets and Systems - Special issue on fuzzy signal processing
Weighted fuzzy mean filters for image processing
Fuzzy Sets and Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Digital Image Processing
Edge detection in multispectral images using the self-organizing map
Pattern Recognition Letters
Suppression of Impulse Noise in Medical Images with the Use of Fuzzy Adaptive Median Filter
Journal of Medical Systems
Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter
Digital Signal Processing
Histogram-based fuzzy filter for image restoration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new efficient approach for the removal of impulse noise from highly corrupted images
IEEE Transactions on Image Processing
MR Images Restoration With the Use of Fuzzy Filter Having Adaptive Membership Parameters
Journal of Medical Systems
Robust impulse-noise filtering for biomedical images using numerical interpolation
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
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This paper presents a new artificial intelligent based neuro-fuzzy rule base adaptive median filter for removing highly impulse noise. Since the filter is rule base, it is called neuro-fuzzy rule base adaptive median (NFRBAM) filter. The NFRBAM filter is an improved version of switch mode fuzzy adaptive median filter (SMFAMF) and is presented for the purpose of noise reduction of images corrupted with additive impulse noise. The NFRBAM filter consists of a decision unit and three different types of filters. In the decision unit, the noisy input image is directed to the proper filter with respect to the noise density. Neuro-fuzzy rule based approach is used in both decision and filtering parts. In artificial neural network, multi layer perceptron (MLP) architecture with backpropagation (BP) algorithm is used for noise detection and removing highly impulse noise corrupted MR images. In fuzzy logic, bell-shaped membership function is employed in order to obtain better results. Experimental results indicate that the proposed filter is improvable with the increased fuzzy rules to reduce more noise corrupted images and preserve image details more than SMFAMF.