Abstract
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.
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Cui, J., Qi, J., Li, D. (2013). A Fast Approximate Sparse Coding Networks and Application to Image Denoising. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_74
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DOI: https://doi.org/10.1007/978-3-642-39065-4_74
Publisher Name: Springer, Berlin, Heidelberg
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