Non-local Network Routing for Perceptual Image Super-Resolution

Zexin Ji, Xin Dong, Zhendong Li, Zekuan Yu, Hao Liu

Abstract: In this paper, we propose a non-local network routing (NNR) approach for perceptual image super-resolution. Unlike conventional methods which generate visually-faked textures due to exiting hand-designed losses, our approach aims to globally optimize both procedures of learning an optimal perceptual loss and routing a spatial-adaptive network architecture in a unified reinforcement learning framework. To this end, we introduce a reward function to teach our objective to pay more attention on the visual quality of the super-resolved image. Moreover, we carefully design an offset operation inside the neural architecture search space, which typically deforms the receptive field on boundary refinement in a non-local manner. Experimentally, our proposed method surpasses the perceptual performance over state-of-the-art methods on several widely-evaluated benchmark datasets.