Single-image super resolution using evolutionary sparse coding technique

Sparse coding (SC) has recently become a widely used tool in signal and image processing. The sparse linear combination of elements from an appropriately chosen over-complete dictionary can represent many signal patches. SC applications have been explored in many fields such as image super resolution (SR), image-feature extraction, image reconstruction, and segmentation. In most of these applications, learning-based SC has provided an excellent image quality. SC involves two steps: dictionary construction and searching the dictionary using quadratic programming. This study focuses on the searching step and a new adaptive variation of genetic algorithm is proposed to search and find the optimum closest match in the dictionary. Also, inspired by the proposed evolutionary SC (ESC), a single-image SR algorithm is proposed. A sparse representation for each patch of the low-resolution input image is obtained by ESC and it is used to generate the high-resolution output image. Experimental results show that the proposed ESC-based method would lead to a better SR image quality.