Self-similarity based image super-resolution on frequency domain

Self-similarity has been popularly exploited for image super resolution in recent years. Image is decomposed into LF (low frequency) and HF (high frequency) components, and similar patches are searched in the LF domain across the pyramid scales of the original image. Once a similar LF patch is found, the LF is combined with the corresponding HR patch, and we reconstruct the HR (high resolution) version. In this paper, we separately search similar LR and HR patches in the LF and HF domains, respectively. In addition, self-similarity based SR is applied to the new structure-texture domain instead of the existing LF and HF. Experimental results show that the proposed method outperforms several conventional SR algorithms based on self-similarity.