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Sparse principle component analysis for single image super-resolution

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.