Variance training data in image enhancement

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dc.contributor.authorNgernplubpla, Jaturon
dc.contributor.authorChitsobhuk, Orachat
dc.date.accessioned2026-08-06T10:24:55Z
dc.date.available2026-08-06T10:24:55Z
dc.date.issued2019-07-01
dc.description.abstractThis paper presents a study of neuro-fuzzy behavior in clustering gradient profile spectral characteristics. Various types of image scene are chosen to evaluate neuro-fuzzy performance. The combinations of training data subsets are learned by ANFIS model to generate gradient profile priors, which are used as optimum weight selection criteria for image enhancement. The experimental results illustrate quantitative performance improvement and perceptual improvement in recovery of the high-resolution details in various images.
dc.identifier.citationProceedings of the 16th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2019, 943-946, 2019
dc.identifier.doi10.1109/ECTI-CON47248.2019.8955129
dc.identifier.other2-s2.0-85078859845
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/9956
dc.sourceProceedings of the 16th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2019
dc.subjectGradient prior
dc.subjectGradient profile generation
dc.subjectImage enhancement
dc.subjectNeuro-fuzzy clustering
dc.titleVariance training data in image enhancement
dc.typeConference Paper

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