According to the image quality degradation in super-resolution reconstruction, we present a new algorithm for a single image super-resolution reconstruction to improve the image resolution. Considering the limitations that the extracted features of low-resolution image can not adapt to image direction changes, we propose a new feature-extracted method, and we build a new global optimization framework with analysis sparse prior based on the multiple extracted features, joint dictionary learning. Experiments show the effectiveness of our method.
Index Terms -resolution reconstruction, sparse representation, multi-features extraction, sparse prior..