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    National Tsing Hua University Institutional Repository > 電機資訊學院 > 電機工程學系 > 期刊論文 >  Self-Learning Based Image Decomposition with Applications to Single Image Denoising


    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/83639


    Title: Self-Learning Based Image Decomposition with Applications to Single Image Denoising
    Authors: De-An Huang;Li-Wei Kang;Wang, Y.-C.F.;Chia-Wen Lin
    教師: 林嘉文
    Date: 2014
    Publisher: Institute of Electrical and Electronics Engineers
    Relation: IEEE Transactions on Multimedia, Institute of Electrical and Electronics Engineers, Volume 16, Issue 1, Jan. 2014, Pages 83-93
    Keywords: Denoising
    image decomposition
    rain removal
    self-learning
    sparse representation
    Abstract: Decomposition of an image into multiple semantic components has been an effective research topic for various image processing applications such as image denoising, enhancement, and inpainting. In this paper, we present a novel self-learning based image decomposition framework. Based on the recent success of sparse representation, the proposed framework first learns an over-complete dictionary from the high spatial frequency parts of the input image for reconstruction purposes. We perform unsupervised clustering on the observed dictionary atoms (and their corresponding reconstructed image versions) via affinity propagation, which allows us to identify image-dependent components with similar context information. While applying the proposed method for the applications of image denoising, we are able to automatically determine the undesirable patterns (e.g., rain streaks or Gaussian noise) from the derived image components directly from the input image, so that the task of single-image denoising can be addressed. Different from prior image processing works with sparse representation, our method does not need to collect training image data in advance, nor do we assume image priors such as the relationship between input and output image dictionaries. We conduct experiments on two denoising problems: single-image denoising with Gaussian noise and rain removal. Our empirical results confirm the effectiveness and robustness of our approach, which is shown to outperform state-of-the-art image denoising algorithms.
    Relation Link: http://www.ieee.org/
    URI: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/83639
    Appears in Collections:[電機工程學系] 期刊論文
    [光電研究中心] 期刊論文

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