A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information

• URL: http://arxiv.org/abs/2212.04314v1
• Date: Thu, 8 Dec 2022 15:10:49 GMT
• Title: A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information
• Authors: Jing Fang, Yinbo Yu, Zhongyuan Wang, Xin Ding, Ruimin Hu
• Abstract summary: Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images. In this paper, we study image features in the frequency domain to design a novel scale-arbitrary image SR network.
• Score: 42.55177009667711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images. Spatial-domain information has been widely exploited to implement image SR, so a new trend is to involve frequency-domain information in SR tasks. Besides, image SR is typically application-oriented and various computer vision tasks call for image arbitrary magnification. Therefore, in this paper, we study image features in the frequency domain to design a novel scale-arbitrary image SR network. First, we statistically analyze LR-HR image pairs of several datasets under different scale factors and find that the high-frequency spectra of different images under different scale factors suffer from different degrees of degradation, but the valid low-frequency spectra tend to be retained within a certain distribution range. Then, based on this finding, we devise an adaptive scale-aware feature division mechanism using deep reinforcement learning, which can accurately and adaptively divide the frequency spectrum into the low-frequency part to be retained and the high-frequency one to be recovered. Finally, we design a scale-aware feature recovery module to capture and fuse multi-level features for reconstructing the high-frequency spectrum at arbitrary scale factors. Extensive experiments on public datasets show the superiority of our method compared with state-of-the-art methods.

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