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http://dx.doi.org/10.15207/JKCS.2020.11.3.019

Super Resolution using Dictionary Data Mapping Method based on Loss Area Analysis  

Han, Hyun-Ho (College of General Education, University of Ulsan)
Lee, Sang-Hun (Ingenium College of Liberal Arts, Kwangwoon University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.3, 2020 , pp. 19-26 More about this Journal
Abstract
In this paper, we propose a method to analyze the loss region of the dictionary-based super resolution result learned for image quality improvement and to map the learning data according to the analyzed loss region. In the conventional learned dictionary-based method, a result different from the feature configuration of the input image may be generated according to the learning image, and an unintended artifact may occur. The proposed method estimate loss information of low resolution images by analyzing the reconstructed contents to reduce inconsistent feature composition and unintended artifacts in the example-based super resolution process. By mapping the training data according to the final interpolation feature map, which improves the noise and pixel imbalance of the estimated loss information using a Gaussian-based kernel, it generates super resolution with improved noise, artifacts, and staircase compared to the existing super resolution. For the evaluation, the results of the existing super resolution generation algorithms and the proposed method are compared with the high-definition image, which is 4% better in the PSNR (Peak Signal to Noise Ratio) and 3% in the SSIM (Structural SIMilarity Index).
Keywords
Super Resolution; Example-based; Learning; Dictionary; PSNR; SSIM;
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Times Cited By KSCI : 12  (Citation Analysis)
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