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Author: search.filters.author.Chitsobhuk, OrachatSubject: search.filters.subject.Image enhancement

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    Variance training data in image enhancement
    (2019-07-01)
    Ngernplubpla, Jaturon
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    Chitsobhuk, Orachat
    This 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.
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    Image enhancement based on edge boosting algorithm
    (2015-01-01)
    Ngernplubpla, Jaturon
    ;
    Chitsobhuk, Orachat
    In this paper, a technique for image enhancement based on proposed edge boosting algorithm to reconstruct high quality image from a single low resolution image is described. The difficulty in single-image super-resolution is that the generic image priors resided in the low resolution input image may not be sufficient to generate the effective solutions. In order to achieve a success in super-resolution reconstruction, efficient prior knowledge should be estimated. The statistics of gradient priors in terms of priority map based on separable gradient estimation, maximum likelihood edge estimation, and local variance are introduced. The proposed edge boosting algorithm takes advantages of these gradient statistics to select the appropriate enhancement weights. The larger weights are applied to the higher frequency details while the low frequency details are smoothed. From the experimental results, the significant performance improvement quantitatively and perceptually is illustrated. It can be seen that the proposed edge boosting algorithm demonstrates high quality results with fewer artifacts, sharper edges, superior texture areas, and finer detail with low noise.