CLBSR: A deep curriculum learning-based blind image super resolution network using geometrical prior

Abstract

Blind image super resolution (SR) is a challenging computer vision task, which involves enhancing the quality of the low-resolution (LR) images obtained by various degradation operations. Deep neural networks have provided state-of-the-art performances for the task of image SR in a blind fashion. It has been shown in the literature that by decoupling the task of blind image SR into the blurring kernel estimation and high-quality image reconstruction, superior performance can be obtained. In this paper, we first propose a novel optimization problem that, by using the geometrical information as prior, is able to estimate the blurring kernels in an accurate manner. We then propose a novel blind image SR network that employs the blurring kernel thus estimated in its network architecture and learning algorithm in order to generate high-quality images. In this regard, we utilize the curriculum learning strategy, wherein the training process of the SR network is initially facilitated by using the ground truth (GT) blurring kernel and then continued with the estimated blurring kernel obtained from our optimization problem. The results of various experiments show the effectiveness of the proposed blind image SR scheme in comparison to state-of-the-art methods on various degradation operations and benchmark datasets.