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Bogdan Smolka
Researcher at Silesian University of Technology
Publications - 227
Citations - 3250
Bogdan Smolka is an academic researcher from Silesian University of Technology. The author has contributed to research in topics: Median filter & Noise reduction. The author has an hindex of 24, co-authored 224 publications receiving 3040 citations. Previous affiliations of Bogdan Smolka include University of Toronto.
Papers
More filters
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Selection weighted vector directional filters
TL;DR: The proposed angular optimization algorithms take advantage of adaptive stack filters design and weighted median filtering framework and are able to remove image noise, while maintaining excellent signal-detail preservation capabilities and sufficient robustness for a variety of signal and noise statistics.
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cDNA microarray image processing using fuzzy vector filtering framework
TL;DR: Simulation studies reported in this paper indicate that the proposed adaptive fuzzy vector filters are computationally attractive, yield excellent performance and are able to preserve structural information while efficiently suppressing noise in cDNA microarray data.
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A Statistically-Switched Adaptive Vector Median Filter
TL;DR: The analysis and experimental results indicate that the proposed filter is capable of detecting and removing impulsive noise in multichannel images and is computationally efficient and provides excellent balance between the noise attenuation and signal-detail preservation.
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A multichannel order-statistic technique for cDNA microarray image processing
TL;DR: An automated image processing procedure capable of processing complementary deoxyribonucleic acid (cDNA) microarray images by employing nonlinear filtering solutions based on robust order statistics and yields excellent performance by removing noise and enhancing spot location determination.
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Self-adaptive algorithm for segmenting skin regions
TL;DR: A new self-adaptive algorithm for segmenting human skin regions in color images that learns a local skin color model on the fly and takes advantage of textural features for computing local propagation costs that are used in the distance transform.