FRISPEE: FRI-Based Single Image Super-Resolution with Deep Recursive Residual Network

Abstract

In this paper, we propose a novel single image super-resolution algorithm that integrates a model-based approach with self-learning deep networks. The proposed method can be adapted to low-resolution (LR) images obtained with real acquisition devices where the point spread function is Gaussian-like. By modelling natural image lines as piece-wise smooth functions and approximating the blurring kernel with B-splines, an intermediate high-resolution (HR) image can be first obtained based on Finite Rate of Innovation theory. A self-supervised deep recursive residual network is then applied to further enhance the reconstruction quality. From the simulation results, our algorithm outperforms other self-learning algorithms and achieves state-of-the-art performance.