  Lightweight neural networks for single-image super-resolution (SISR) tasks
have made substantial breakthroughs in recent years. Compared to low-frequency
information, high-frequency detail is much more difficult to reconstruct. Most
SISR models allocate equal computational resources for low-frequency and
high-frequency information, which leads to redundant processing of simple
low-frequency information and inadequate recovery of more challenging
high-frequency information. We propose a novel High-Frequency Focused Network
(HFFN) through High-Frequency Focused Blocks (HFFBs) that selectively enhance
high-frequency information while minimizing redundant feature computation of
low-frequency information. The HFFB effectively allocates more computational
resources to the more challenging reconstruction of high-frequency information.
Moreover, we propose a Local Feature Fusion Block (LFFB) effectively fuses
features from multiple HFFBs in a local region, utilizing complementary
information across layers to enhance feature representativeness and reduce
artifacts in reconstructed images. We assess the efficacy of our proposed HFFN
on five benchmark datasets and show that it significantly enhances the
super-resolution performance of the network. Our experimental results
demonstrate state-of-the-art performance in reconstructing high-frequency
information while using a low number of parameters.
