A novel multi-image super-resolution reconstruction method using anisotropic fractional order adaptive norm

A high-resolution image is obtained by fusing the information derived from blurred, sub-pixel shifted, and noisy low-resolution observations. In this paper, a novel regularization model based on an Anisotropic Fractional Order Adaptive (AFOA) norm is proposed and then we apply the AFOA model into the Super-Resolution Reconstruction technology. Compared with the existing models, the proposed AFOA model can remove the noise and protect the edges adaptively according to the local features of the images. Meanwhile, the proposed AFOA model can avoid the staircase effect effectively in the smooth region. To obtain the solution to the proposed AFOA model, the Gradient Descent Method is used in this paper. Finally, the experimental results show that the proposed method has much improvement than the existing methods in the respect of the Peak Signal-to-Noise Ratio and the visual quality.