Improved image super resolution reconstruction method based on multiple regularizations

For better balancing the problems of noise-reducing and details-keeping in the process of the Super-Resolution (SR) image reconstruction, this paper proposes a novel multiple regularizations-based SR image reconstruction method, which combines an Amended Non-Local Total Variation (ANLTV) and Total Variation (TV) regularizations in the SR image reconstruction framework. Firstly, according to heavy-tailed distribution properties of the natural images, the ANLTV regularization is given by reformulating the weighting coefficients of the traditional Non-Local Total Variation (NLTV) with the combination of the Gaussian, Laplacian and Cauchy distributions. Accordingly, the initial SR image is then reconstructed using the split Bregman algorithm, from which, the final SR image is obtained by deblurring the initial reconstructed image with a TV regularization. In order to verify the performance of the proposed algorithm, the quantitative comparison between the proposed method and the traditional TV and NLTV based methods are carried out. The experimental results illustrate that the peak signal to noise ratio, signal to noise ratio and structural similarity obtained by the proposed method is obviously higher than the traditional ones, which is able to satisfy the requirement of reducing noise and preserving edge details in SR reconstruction simultaneously.