  Single image super-resolution(SISR) is an ill-posed problem that aims to
obtain high-resolution (HR) output from low-resolution (LR) input, during which
extra high-frequency information is supposed to be added to improve the
perceptual quality. Existing SISR works mainly operate in the spatial domain by
minimizing the mean squared reconstruction error. Despite the high peak
signal-to-noise ratios(PSNR) results, it is difficult to determine whether the
model correctly adds desired high-frequency details. Some residual-based
structures are proposed to guide the model to focus on high-frequency features
implicitly. However, how to verify the fidelity of those artificial details
remains a problem since the interpretation from spatial-domain metrics is
limited. In this paper, we propose FreqNet, an intuitive pipeline from the
frequency domain perspective, to solve this problem. Inspired by existing
frequency-domain works, we convert images into discrete cosine transform (DCT)
blocks, then reform them to obtain the DCT feature maps, which serve as the
input and target of our model. A specialized pipeline is designed, and we
further propose a frequency loss function to fit the nature of our
frequency-domain task. Our SISR method in the frequency domain can learn the
high-frequency information explicitly, provide fidelity and good perceptual
quality for the SR images. We further observe that our model can be merged with
other spatial super-resolution models to enhance the quality of their original
SR output.
