Image super-resolution via saliency sparse representation

The paper proposes a new approach to single-image super resolution (SR), which is based on sparse representation. Previous researchers just focus on the global intensive patch, without local intensive patch. The performance of dictionary trained by the local saliency intensive patch is more significant. Motivated by this, we joined the saliency detection to detect marked area in the image. We proposed a sparse representation for saliency patch of the low-resolution input, and used the coefficients of this representation to generate the high-resolution output. Compared to precious approaches which simply sample a large amount of image patch pairs, the saliency dictionary pair is a more compact representation of the patch pairs, reducing the computational cost substantially. Through the experiment, we demonstrate that our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. © (2014) Trans Tech Publications, Switzerland