  Deep learning based methods, especially convolutional neural networks (CNNs)
have been successfully applied in the field of single image super-resolution
(SISR). To obtain better fidelity and visual quality, most of existing networks
are of heavy design with massive computation. However, the computation
resources of modern mobile devices are limited, which cannot easily support the
expensive cost. To this end, this paper explores a novel frequency-aware
dynamic network for dividing the input into multiple parts according to its
coefficients in the discrete cosine transform (DCT) domain. In practice, the
high-frequency part will be processed using expensive operations and the
lower-frequency part is assigned with cheap operations to relieve the
computation burden. Since pixels or image patches belong to low-frequency areas
contain relatively few textural details, this dynamic network will not affect
the quality of resulting super-resolution images. In addition, we embed
predictors into the proposed dynamic network to end-to-end fine-tune the
handcrafted frequency-aware masks. Extensive experiments conducted on benchmark
SISR models and datasets show that the frequency-aware dynamic network can be
employed for various SISR neural architectures to obtain the better tradeoff
between visual quality and computational complexity. For instance, we can
reduce the FLOPs of EDSR model by approximate $50\%$ while preserving
state-of-the-art SISR performance.
