Across-resolution adaptive dictionary learning for single-image super-resolution

摘要

This paper proposes a novel adaptive dictionary learning approach for a single-image super-resolution based on a sparse representation. The adaptive dictionary learning approach of the sparse representation is very powerful, for image restoration such as image denoising. The existing adaptive dictionary learning requires training image patches which have the same resolution as the output image. Because of this requirement, the adaptive dictionary learning for the single-image super-resolution is not trivial, since the resolution of the input low-resolution image which can be used for the adaptive dictionary learning is essentially different from that of the output high-resolution image. It is known that natural images have high across-resolution patch redundancy which means that we can find similar patches within different resolution images. Our experimental comparisons demonstrate that the proposed across-resolution adaptive dictionary learning approach outperforms state-of-the-art single-image super-resolutions.