Efficient distortion reduction of mixed noise filters by neuro-fuzzy processing

Authors:
M. Emin Yüksel;Alper Baştürk
Affiliations:
Digital Signal and Image Processing Lab., Dept. of Electrical and Electronics Eng., Erciyes University, Kayseri, Turkey;Digital Signal and Image Processing Lab., Dept. of Electrical and Electronics Eng., Erciyes University, Kayseri, Turkey
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2006

Citing 9
Cited 0

Structure identification of fuzzy model

Fuzzy Sets and Systems
Detail-preserving median based filters in image processing

Pattern Recognition Letters
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems

Neural Networks
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom

Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
Digital Median Filters

Journal of VLSI Signal Processing Systems
Detail-preserving restoration of impulse noise corrupted images by a switching median filter guided by a simple neuro-fuzzy network

EURASIP Journal on Applied Signal Processing
A simple neuro-fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images

IEEE Transactions on Fuzzy Systems
Selective removal of impulse noise based on homogeneity level information

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A simple method for reducing undesirable distortion effects of mixed noise filters for digital images is presented. The method is based on a simple 2-input 1-output neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using simple artificial images generated on a computer. The method can be used with any type of mixed noise filters since its operation is completely independent of the filter. The proposed method is applied to two representative mixed noise filters from the literature under different noise conditions and image properties. Results indicate that the proposed method may efficiently be used with any type of mixed noise filters to effectively reduce their distortion effects.