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    NTHUR > College of Electrical Engineering and Computer Science > Department of Electrical Engineering > EE Conference Papers >  Self-learning-based single image super-resolution of a highly compressed image

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    Title: Self-learning-based single image super-resolution of a highly compressed image
    Authors: Li-Wei Kang;Bo-Chi Chuang;Chih-Chung Hsu;Chia-Wen Lin;Chia-Hung Yeh
    Teacher: 林嘉文
    Date: 2013
    Publisher: Institute of Electrical and Electronics Engineers
    Relation: IEEE 15th International Workshop on Multimedia Signal Processing (MMSP), Pula, Sept. 30 2013-Oct. 2 2013, Pages 224 - 229
    Abstract: Low-quality images are usually not only with low-resolution, but also suffer from compression artifacts (blocking artifact is treated as an example in this paper). Directly performing image super-resolution (SR) to a highly compressed (low-quality) image would also simultaneously magnify the blocking artifacts, resulting in unpleasing visual quality. In this paper, we propose a self-learning-based SR framework to simultaneously achieve single-image SR and compression artifact removal for a highly-compressed image. We argue that individually performing deblocking first, followed by SR to an image, would usually inevitably lose some image details induced by deblocking, which may be useful for SR, resulting in worse SR result. In our method, we propose to self-learn image sparse representation for modeling the relationship between low and high-resolution image patches in terms of the learned dictionaries, respectively, for image patches with and without blocking artifacts. As a result, image SR and deblocking can be simultaneously achieved via sparse representation and MCA (morphological component analysis)-based image decomposition. Experimental results demonstrate the efficacy of the proposed algorithm.
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    Appears in Collections:[Department of Electrical Engineering] EE Conference Papers
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