Optimized image super-resolution based on sparse representation

This paper presents a new approach to image superresolution based on sparse representation. This problem is formulated as a compressive sensing system, in which an over-complete dictionary is used to sparsely represent a low resolution image and generate a high resolution image. We propose a method to adaptively construct training images by selecting the most correlative images based on scale-invariant feature transform(SIFT). In this way, the sampling and dictionary training process is accelerated and optimized. In comparison with the existing approaches, experimental results show that the proposed method outperforms the existing benchmark in terms of both super-resolution quality and the processing time, which makes the propose method suitable for practical applications.