Detail-preserving median based filters in image processing
Pattern Recognition Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Combining vector median and vector directional filters: the directional-distance filters
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
EURASIP Journal on Applied Signal Processing
Memoryless discrete-time signal detection in long-range dependent noise
IEEE Transactions on Signal Processing
Vector directional filters-a new class of multichannel image processing filters
IEEE Transactions on Image Processing
A framework for application of neuro-case-rule base hybridization in medical diagnosis
Applied Soft Computing
Video sequence motion tracking by fuzzification techniques
Applied Soft Computing
Heuristic wavelet shrinkage for denoising
Applied Soft Computing
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Based on an integration of a simple impulse detector and a robust neuro-fuzzy (RNF) network, an effective impulse noise filter for color images is presented. It consists of two modes of operation, namely, training and testing (filtering). During training, the impulse detector is used to locate the noisy pixels in the color images for optimizing the RNF network. During testing, if a pixel is detected as a corrupted one according to the impulse detector, the trained RNF network will be triggered to output a new pixel to replace it. The proposed impulse noise filter is distinguished by two properties. The first is the use of a simple impulse detector, which is efficient and yet effective in detecting the noisy pixels in color images. The other is the use of a novel membership function in the design of the adaptive RNF network, making the network robust to impulse noise. As demonstrated by the experimental results, the proposed filter not only has the abilities of noise attenuation and details preservation but also possesses desirable robustness and adaptive capabilities. It outperforms other conventional multichannel filters.