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Subject: search.filters.subject.Image Enhancement

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    Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN
    (2021-01-01)
    Intaniyom, Tanakit
    ;
    Thananporn, Warinthorn
    ;
    Woraratpanya, Kuntpong
    Anime is a storytelling medium similar to movies and books. Anime images are a kind of artworks, which are almost entirely drawn by hand. Hence, reproducing existing Anime with larger sizes and higher quality images is expensive. Therefore, we proposed a model based on convolutional neural networks to extract outstanding features of images, enlarge those images, and enhance the quality of Anime images. We trained the model with a training set of 160 images and a validation set of 20 images. We tested the trained model with a testing set of 20 images. The experimental results indicated that our model successfully enhanced the image quality with a larger image-size when compared with the common existing image enlargement and the original SRCNN method.
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    Neuro-fuzzy profile clustering in image enhancement
    (2019-03-01)
    Ngernplubpla, Jaturon
    ;
    Chitsobhuk, Orachat
    This 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.
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    Image super resolution with adaptive edge enhancement algorithm
    (2014-02-24)
    Ngernplubpla, Jaturon
    ;
    Chitsobhuk, Orachat
    In this paper, an adaptive edge enhancement algorithm is proposed to reconstruct a super resolution image from a single low resolution one. In order to improve the results of the high resolution reconstruction, edge statistics is learned from the scenes using a statistical analysis of the maximum likelihood estimation to approximate edge boosting weight that helps to significantly enhance edge information in the high frequency area. The edge sketch image will be adaptively combined with the results of wiener filter according to the values of the local variance. The experimental results on several test images show the success in reconstructing the super resolution both quantitatively and perceptually. © 2014 Copyright SPIE.