Structure identification of fuzzy model
Fuzzy Sets and Systems
Digital image processing
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Adaptive fuzzy switching filter for images corrupted by impulse noise
Pattern Recognition Letters
A smoothing principle for the Huber and other location M-estimators
Computational Statistics & Data Analysis
Nonlocal-means image denoising technique using robust M-estimator
Journal of Computer Science and Technology
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Tri-state median filter for image denoising
IEEE Transactions on Image Processing
Noise adaptive soft-switching median filter
IEEE Transactions on Image Processing
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization
IEEE Transactions on Image Processing
A universal noise removal algorithm with an impulse detector
IEEE Transactions on Image Processing
Robust estimation approach for blind denoising
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Detection Statistic for Random-Valued Impulse Noise
IEEE Transactions on Image Processing
Adaptive median filters: new algorithms and results
IEEE Transactions on Image Processing
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In this paper, a robust 2-stage impulse noise removal system is proposed to remove impulse noise from extremely corrupted images. The contributions are in two-fold. First, a neuro-fuzzy based impulse noise detector (NFIDET) is introduced to identify the noisy pixels. NFIDET is a powerful noise detector that can handle image corruption even up to 90% with zero miss and false detection rate with a simple neuro-fuzzy structure. This is the best result among the other impulse noise detectors in the literature. Second, this paper presents a new approach for weight calculation of adaptive weighted mean filter by using robust statistical model. An adaptive robust weighted mean (ARWM) filter removes a detected noisy pixel by adaptively determining filtering window size and replacing a noisy pixel with the weighted mean of the noise-free pixels in its window. A Geman-McClure robust estimation function is used to estimate the weights of the pixels. Simulation results also show that the proposed robust filter substantially outperforms many other existing algorithms in terms of image restoration.