  Image super-resolution (SR) methods essentially lead to a loss of some
high-frequency (HF) information when predicting high-resolution (HR) images
from low-resolution (LR) images without using external references. To address
this issue, we additionally utilize online retrieved data to facilitate image
SR in a unified deep framework. A novel dual high-frequency recovery network
(DHN) is proposed to predict an HR image with three parts: an LR image, an
internal inferred HF (IHF) map (HF missing part inferred solely from the LR
image) and an external extracted HF (EHF) map. In particular, we infer the HF
information based on both the LR image and similar HR references which are
retrieved online. For the EHF map, we align the references with affine
transformation and then in the aligned references, part of HF signals are
extracted by the proposed DHN to compensate for the HF loss. Extensive
experimental results demonstrate that our DHN achieves notably better
performance than state-of-the-art SR methods.
