Neuro-fuzzy profile clustering in image enhancement

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dc.contributor.authorNgernplubpla, Jaturon
dc.contributor.authorChitsobhuk, Orachat
dc.date.accessioned2026-08-06T10:24:10Z
dc.date.available2026-08-06T10:24:10Z
dc.date.issued2019-03-01
dc.description.abstractThis paper proposes a technique for clustering features into profile groups to obtain optimum enhancement weights for reconstructing high resolution images. Neuro-fuzzy model, which combines the fuzzy reasoning behavior with the adaptive learning capability and connectionist structure of neural networks, is adopted to analyze and learn with gradient data and statistics and to generate gradient profile priors. In enhancement process, the optimum weights are appropriately chosen according to the gradient profile priors. From the experimental results, the proposed algorithm demonstrates quantitative performance improvement in classifying data and perceptual improvement in recovery of the high-resolution image.
dc.identifier.citationIeecon 2019 7th International Electrical Engineering Congress Proceedings, 2019
dc.identifier.doi10.1109/iEECON45304.2019.8938965
dc.identifier.other2-s2.0-85077961390
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/9753
dc.sourceIeecon 2019 7th International Electrical Engineering Congress Proceedings
dc.subjectGradient prior
dc.subjectGradient profile generation
dc.subjectImage Enhancement
dc.subjectNeuro-fuzzy clustering
dc.titleNeuro-fuzzy profile clustering in image enhancement
dc.typeConference Paper

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