Abstract
With the development of sparse coding and compressive sensing, image Super-resolution (SR) reconstruction attracts extensive attentions. In this paper, we mainly focus on recovering super-resolution version given only one single low-resolution (LR) image. The proposed method is combined with the example-based algorithm, which also exploits the relationship between the low image patches and the high image patches. Firstly, the proposed method applies guided filter, the first-order and second-order derivatives to extract multiple features from LR images, which superior to using only one feature space. Then, the effective dictionary is constructed by a novel algorithm called Relaxation K-SVD (R-KSVD). R-KSVD relaxes the constraints of Orthogonal Matching Pursuit method (R-OMP) in training dictionary for K-SVD algorithm. Finally, a new approach is presented to estimating better HR residual image in the Back Projection. Experimental results demonstrate the superiority of our algorithm in both visual fidelity and numerical measures.
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Fu, F. et al. (2013). Super-Resolution from One Single Low-Resolution Image Based on R-KSVD and Example-Based Algorithm. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_5
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DOI: https://doi.org/10.1007/978-3-642-41278-3_5
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