Resolution-Aware Network for Image Super-Resolution

In existing deep network-based image superresolution (SR) methods, each network is only trained for a fixed upscaling factor and can hardly generalize to unseen factors at test time, which is non-scalable in real applications. To mitigate this issue, this paper proposes a resolution-aware network (RAN) for simultaneous SR of multiple factors. The key insight is that SR of multiple factors is essentially different but also shares common operations. To attain stronger generalization across factors, we design an upsampling network (U-Net) consisting of several sub-modules, in which each sub-module implements an intermediate step of the overall image SR and can be shared by SR of different factors. A decision network (D-Net) is further adopted to identify the quality of the input low-resolution image and adaptively select suitable sub-modules to perform SR. U-Net and D-Net together constitute the proposed RAN model, and are jointly trained using a new hierarchical loss function on SR tasks of multiple factors. Experimental evaluations demonstrate that the proposed RAN compares favorably against the state-of-the-art methods and its performance can well generalize across different upscaling factors.