An anisotropic edge total generalized variation energy super-resolution based on fast l₁-norm dictionary edge representations

For camera-based imaging, low resolution and noise outliers are the major challenges. Here, we propose a novel super-resolution method－total generalized variation (TGV) super-resolution based on fast l₁-norm dictionary edge representations. First, anisotropic diffusion tensor (ADT) is utilized as high frequency edge information. The fast l₁-norm dictionary representation method is used to create dictionaries of LR image and the corresponding high frequency edge information. This method can quickly build dictionaries on the same database, and avoid the influence of outliers. Then we combine the edge information ADT and TGV model as the new regularization function. Finally, the super-resolution cost function is established. The results show that the algorithm has high feasibility and robustness to simulation data and SO12233 target data. It can effectively remove noise outliers and obtain high-quality clear images. Compared with other classical algorithms, the proposed algorithm can obtain higher PSNR and SSIM values.