Detail-preserving median based filters in image processing
Pattern Recognition Letters
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Median filter based on fuzzy rules and its application to image restoration
Fuzzy Sets and Systems - Special issue on fuzzy signal processing
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Neural Computation
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IEEE Transactions on Image Processing
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IEEE Transactions on Image Processing
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
A new adaptive center weighted median filter for suppressing impulsive noise in images
Information Sciences: an International Journal
Detail-preserving image information restoration guided by SVM based noise mapping
Digital Signal Processing
Progressive decision-based mean type filter for image noise suppression
Computer Standards & Interfaces
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Pattern Recognition Letters
Fundamenta Informaticae - Swarm Intelligence
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Expert Systems with Applications: An International Journal
Image filtering using support vector machine
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Fundamenta Informaticae - Swarm Intelligence
Partition-based fuzzy median filter based on adaptive resonance theory
Computer Standards & Interfaces
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In this letter, a novel adaptive filter, the adaptive two-pass median (ATM) filter based on support vector machines (SVMs), is proposed to preserve more image details while effectively suppressing impulse noise for image restoration. The proposed filter is composed of a noise decision maker and two-pass median filters. Our new approach basically uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a corrupted pixel, the noise-free reduction median filter will be triggered to replace it. Otherwise, it remains unchanged. Then, to improve the quality of the restored image, a decision impulse filter is put to work in the second-pass filtering procedure. As for the noise suppressing both fixed-valued and random-valued impulses without degrading the quality of the fine details, the results of our extensive experiments demonstrate that the proposed filter outperforms earlier median-based filters in the literature. Our new filter also provides excellent robustness at various percentages of impulse noise.