Measurably Stronger Explanation Reliability Via Model Canonization

Deep learning based methods have received a great deal of interest in recent years to solve the single image super-resolution (SISR) problem and their performance is proven to be superior when compared to classical SR techniques. Yet, most of these methods fail to generalize well on real life image datasets because they are trained on synthetic datasets with a small range of blur kernels. This makes data-driven approaches inherently weak when it comes to real images. Therefore, applying image super-resolution independently of the blur kernel is still a challenging task. in this paper we propose IKR-Net, Iterative Kernel Reconstruction network, for blind SISR. in the proposed approach, kernel estimation and high resolution image reconstruction are carried out iteratively using deep models. The iterative refinement provides significant improvement in both the reconstructed image and the estimated blur kernel. IKR-Net achieves state-of-the-art results in blind SISR, especially for images with motion blur.