National Tsing Hua University Institutional Repository:Self-learning-based single image super-resolution of a highly compressed image
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    National Tsing Hua University Institutional Repository > 電機資訊學院 > 電機工程學系 > 會議論文  >  Self-learning-based single image super-resolution of a highly compressed image


    題名: Self-learning-based single image super-resolution of a highly compressed image
    作者: Li-Wei Kang;Bo-Chi Chuang;Chih-Chung Hsu;Chia-Wen Lin;Chia-Hung Yeh
    教師: 林嘉文
    日期: 2013
    出版者: Institute of Electrical and Electronics Engineers
    關聯: IEEE 15th International Workshop on Multimedia Signal Processing (MMSP), Pula, Sept. 30 2013-Oct. 2 2013, Pages 224 - 229
    摘要: 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.
    顯示於類別:[電機工程學系] 會議論文
    [光電研究中心] 會議論文


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