Sparse principle component analysis for single image super-resolution

Qianying Zhang; Jitao Wu

doi:10.1117/12.2178753

4 March 2015 Sparse principle component analysis for single image super-resolution

Qianying Zhang, Jitao Wu

Proceedings Volume 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014); 94430Y (2015) https://doi.org/10.1117/12.2178753
Event: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2014, Beijing, China

Abstract

In this paper, we propose a novel image super-resolution method based on sparse principle component analysis. Various coupled sub-dictionaries are trained to represent high-resolution and low-resolution image patches. The proposed method simultaneously exploits the incoherence of the sub-dictionaries and nonlocal self-similarity existing in natural images. The purpose of introducing these two regularization terms is to design a novel dictionary learning algorithm for having good reconstruction. Furthermore, in the dictionary learning process, the algorithm can update the dictionary as a whole and reduce the computational cost significantly. Experimental results show the efficiency of the proposed method compared to the existing algorithms in terms of both PSNR and visual perception.

Citation Download Citation

Qianying Zhang and Jitao Wu "Sparse principle component analysis for single image super-resolution", Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94430Y (4 March 2015); https://doi.org/10.1117/12.2178753

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