A robust two-stage super-resolution algorithm

Super-Resolution is the process of constructing a high resolution image when a set of one or more low resolution input images is given. Traditionally, there are two methods exploited widely for enhancing the image via Super-Resolution viz. Single-Frame or Single-Based approach and Multi-Frame or Sequence-Based approach. Because the low resolution images have less information because of lower pixel density than their high resolution counterparts, the enhancement process requires missing image data to be calculated. In this paper, we have proposed a novel method that exploits the advantages of both these traditional methods. In the first phase, we improve a set of low resolution images via learning dictionary single frame method and in second phase we combine these by projecting these images onto convex sets thereby enhancing the image by information procured from multiple images. Experimental results show that our method works considerably better than state-of-the art Super Resolution enhancement methods.