National Tsing Hua University Institutional Repository:Single-frame-based rain removal via image decomposition
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    Title: Single-frame-based rain removal via image decomposition
    Authors: Yu-Hsiang Fu;Li-Wei Kang;Chia-Wen Lin;Chiou-Ting Hsu
    Teacher: 林嘉文
    Date: 2011
    Publisher: Institute of Electrical and Electronics Engineers
    Relation: IEEE Int. Conf. Acoustics, Speech & Signal Processing, Prague, Czech Republic, 22-27 May 2011, Pages 1453 - 1456
    Keywords: Rain removal
    sparse coding
    dictionary learning
    image decomposition
    morphological component analysis (MCA)
    Abstract: Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single image has been rarely studied in the literature, where no temporal information among successive images can be exploited, making it more challenging. In this paper, to the best of our knowledge, we are among the first to propose a single-frame-based rain removal framework via properly formulating rain removal as an image decomposition problem based on morphological component analysis (MCA). Instead of directly applying conventional image decomposition technique, we first decompose an image into the low-frequency and high frequency parts using a bilateral filter. The high-frequency part is then decomposed into "rain component" and "non rain component" via performing dictionary learning and sparse coding. As a result, the rain component can be successfully removed from the image while preserving most original image details. Experimental results demonstrate the efficacy of the proposed algorithm.
    Relation Link: http:/
    Appears in Collections:[Department of Electrical Engineering] EE Conference Papers
    [Center for Photonles Research ] CPR Conference Papers

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