Fast Image Super-resolution with Sparse Coding
Shandong Vocational College of Science and Technology, Weifang, 261053, China
In this paper, we introduce a novel fast image reconstruction method for super-resolution (SR) base on sparse coding. This method combine online dictionary learning and a fast sparse coding way, both of which can improve the efficiency of the reconstruction process and ensure the image visual quality. The new online optimization algorithm for dictionary learning based on stochastic approximations, which can drastically advance the learning speed, especially on millions of training samples. Meanwhile, we trained a neural network to speed up the reconstruction process, which based on iterative shrinkage-thresholding algorithm (ISTA), we called learned iterative shrinkage-thresholding algorithm (LISTA). It would produce best approximation sparse code with some fixed depth. We demonstrate that our approach can simultaneously improve the image fidelity and cost less computation.
Key words: SRIR / sparse coding / super-resolution / fast image super-resolution
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.