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论文类型：会议论文

发表时间：2013-07-04

收录刊物：EI、Scopus

卷号：7951 LNCS

期号：PART 1

页面范围：620-626

摘要：Sparse modeling has proven to be an effective and powerful tool that leads to state of the art algorithms in image denoising, inpainting, super-resolution reconstruction, etc. Although various sparse modeling algorithms have been proposed, a major problem of these algorithms is computationally expensive which prohibits them from real-time applications. In this paper, we propose a simple and efficient approach to learn fast approximate sparse coding networks as well as show its application to image denoising. Our experiments demonstrate that the pre-learned network is over 200 times faster than sparse optimization algorithm, and yet obtain approving result in image denoising. ? 2013 Springer-Verlag Berlin Heidelberg.