Super-resolution image reconstruction based on sparse threshold

In order to solve the problem of the time consuming of the super-resolution reconstruction algorithm based on dictionary learning, a method of super-resolution image reconstruction based on sparse threshold model is proposed. First of all, the over-complete dictionary couple based on the theory of joint dictionary by method of sparse threshold is obtained. And then, the sparse representation of feature block image is represented by sparse threshold OMP algorithm. Then, the initial super-resolution image is reconstructed by the high resolution dictionary. Finally, the global optimization of the initial super-resolution image is improved by the modified iterative back projection algorithm, which can improve the quality of reconstructed image. The experimental results show that the average peak signal to noise ratio(PSNR) is 30. 1 dB; the average structure self-similarity( SSIM) is 0. 937 9; the average computation time is 10. 2 s. This method can improve not only the speed of super-resolution reconstruction, but also the quality of reconstructed high resolution images.