Abstract
The conventional super-resolution algorithms on sparse representation reconstruct the high resolution image using one-stage high/low resolution dictionary pairs with inadequate detail information. In order to recover detail information as much as possible, two-stage dictionaries are explored in this paper. Then we train jointly multiple-frequency-band dictionaries consisting of low frequency (LF) dictionaries, middle frequency (MF) dictionaries and high frequency (HF) dictionaries, and simultaneously exploit the prediction relation of LF component, MF component and HF component to recover middle and high frequency information. Considering that there are many repetitive structures in the natural image, nonlocal self-similarity information is combined properly with iterative back-projection procedure to post-process the image. Experimental results demonstrate that the proposed algorithm has remarkable improvement in peak signal-to-noise ratio, structural similarity and visual quality compared with the other learning-based algorithms.
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