Image Super-resolution Reconstruction Based on Adaptive Fractional Order

The reconstruction of super-resolution images from low-resolution images is essentially a morbid invers problem, which can often be dealt with by adding regular terms. In this paper, using the traditional total variational method for reference, fractional total variational regularities and fractional fidelity terms are added to the model to constrain the solution space, and an adaptive fractional order function based on local variance is proposed. In addition, the Fourier transform is used to calculate in the frequency domain, which reduces the computational complexity. The experimental results show that the image reconstruction model proposed in this paper can reconstruct the texture details and edges more clearly, and at the same magnification, the values of peak signal to noise ration (PSNR) and structural similarity index measure(SSIM) are higher than those of the comparison methods.