Single image super-resolution using sparse prior

Abstract

Obtaining high-resolution images from low-resolution ones has been an important topic in computer vision field. This is a very hard problem since low-resolution images will always lose some information when down sampled from high-resolution ones. In this article, we proposed a novel image super-resolution method based on the sparse assumption. Compared to many existing example-based image super-resolution methods, our method is based on single original low-resolution image, i.e. our method does not need any training examples. Compared to other interpolation based approach, like nearest neighbor, bilinear or bicubic, our method takes advantage of the inner properties of high-resolution images, thus obtains a better result. The main approach for our method is based on the recently developed theory called sparse representation and compress sensing. Many experiments show our method can lead to competitive or even superior results in quality to images produced by other super-resolution methods, while our method need much fewer additional information.