A fast approximate sparse coding networks and application to image denoising

Authors:
Jianyong Cui;Jinqing Qi;Dan Li
Affiliations:
School of Information and Communication Engineering, Dalian University of Technology, Dalian, China;School of Information and Communication Engineering, Dalian University of Technology, Dalian, China;School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

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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.