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Subject: search.filters.subject.Convolutional Sparse Coding

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    Distributed compressed video sensing with multiple key frames
    (2026-02-27)
    Nomaguchi, Mizuki
    ;
    Inoue, Ryota
    ;
    Woraratpanya, Kuntpong
    ;
    Kuroki, Yoshimitsu
    Distributed Compressed Video Sensing is a video compression method utilizing Compressed Sensing and Distributed Video Coding. With this method, compressed frames are reconstructed with information obtained by applying Convolutional Sparse Coding to a non-compressed frame. In this study, we aim to increase the reconstruction accuracy by selecting multiple non-compressed frames. In addition, we use symmetric convolution in order to solve a high computational optimization problem. The experimental results show our proposed method outperforms the conventional method.
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    L1-L1 norm-based convolutional sparse coding via Anderson-accelerated Douglas-Rachford splitting
    (2026-02-27)
    Take, Hiroto
    ;
    Furusho, Riku
    ;
    Woraratpanya, Kuntpong
    ;
    Kuroki, Yoshimitsu
    Convolutional Sparse Coding (CSC) represents a signal through the convolution of dictionary filters and sparse coefficients. While the Alternating Direction Method of Multipliers (ADMM) has conventionally been used to solve CSC problems, recent studies have demonstrated that Douglas-Rachford (DR) splitting can achieve faster convergence. In this study, we propose an accelerated CSC algorithm by applying Anderson Acceleration to the DR splitting method. Experimental results demonstrate that the proposed method significantly improves convergence speed compared to standard DR splitting.