A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Detail-preserving median based filters in image processing
Pattern Recognition Letters
Median filter based on fuzzy rules and its application to image restoration
Fuzzy Sets and Systems - Special issue on fuzzy signal processing
Genetic-based fuzzy hybrid multichannel filters for color image restoration
Fuzzy Sets and Systems
Convergence behavior of the LMS algorithm in subband adaptive filtering
Signal Processing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
LUM smoother with smooth control for noisy image sequences
EURASIP Journal on Applied Signal Processing
Fast detection and impulsive noise removal in color images
Real-Time Imaging - Special issue on multi-dimensional image processing
A fast impulsive noise color image filter using fuzzy metrics
Real-Time Imaging - Special issue on multi-dimensional image processing
A new adaptive center weighted median filter for suppressing impulsive noise in images
Information Sciences: an International Journal
Generalized selection weighted vector filters
EURASIP Journal on Applied Signal Processing
Progressive decision-based mean type filter for image noise suppression
Computer Standards & Interfaces
Fuzzy filter based on interval-valued fuzzy sets for image filtering
Fuzzy Sets and Systems
Some improvements for image filtering using peer group techniques
Image and Vision Computing
Information Sciences: an International Journal
Partition-based weighted sum filters for image restoration
IEEE Transactions on Image Processing
Tri-state median filter for image denoising
IEEE Transactions on Image Processing
Selective removal of impulse noise based on homogeneity level information
IEEE Transactions on Image Processing
A universal noise removal algorithm with an impulse detector
IEEE Transactions on Image Processing
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This paper presents a novel partition-based fuzzy median filter for noise removal from corrupted digital images. The proposed filter is obtained as the weighted sum of the current pixel value and the output of the median filter, where the weight is set by using fuzzy rules concerning the state of the input signal sequence to indicate to what extent the pixel is considered to be noise. Based on the adaptive resonance theory, the authors developed a neural network model and created a new weight function where the neural network model is employed to partition the observation vector. In this framework, each observation vector is mapped to one of the M blocks that form the observation vector space. The least mean square (LMS) algorithm is applied to obtain the optimal weight for each block. Experiment results have confirmed the high performance of the proposed filter in efficiently removing impulsive noise and Gaussian noise.