改进的单幅图像的自学习超分辨率重建方法

iming at the limitation of sparse representation depended on large training sample dictionaries for traditional super-resolution reconstruction method, this paper proposed an improved super-resolution reconstruction method for self-learning of single image based on the characteristic of l norm's weak sparsity. Firstly, it used self-learning to established the non pyramid stepped training images; then, it used the custom method to extract feature blocks and feature pixel values of corresponding LR and HR images; finally, combined with the collaborative representation (CR) theory of l norm and support vector regression (SVR) technology, it established mapping model of the super-resolution. Experimental results show that the proposed super-resolution method is not only feasible and effective, but also the average PSNR increases for 0.06~3.92 dB and SSIM increases for 0.002 4~0.034 8 compared with other conventional super-resolution approaches of single image. From the objective and subjective vision, it is proved that the proposed method is excellent.