FourierSR: A Fourier Token-based Plugin for Efficient Image Super-Resolution

Image super-resolution (SR) aims to recover low-resolution images to high-resolution images, where improving SR efficiency is a high-profile challenge. However, commonly used units in SR, like convolutions and window-based Transformers, have limited receptive fields, making it challenging to apply them to improve SR under extremely limited computational cost. To address this issue, inspired by modeling convolution theorem through token mix, we propose a Fourier token-based plugin called FourierSR to improve SR uniformly, which avoids the instability or inefficiency of existing token mix technologies when applied as plug-ins. Furthermore, compared to convolutions and windows-based Transformers, our FourierSR only utilizes Fourier transform and multiplication operations, greatly reducing complexity while having global receptive fields. Experimental results show that our FourierSR as a plug-and-play unit brings an average PSNR gain of 0.34dB for existing efficient SR methods on Manga109 test set at the scale of x4, while the average increase in the number of Params and FLOPs is only 0.6% and 1.5% of original sizes. We will release our codes upon acceptance.

Dual-domain Modulation Network for Lightweight Image Super-Resolution

Lightweight image super-resolution (SR) aims to reconstruct high-resolution images from low-resolution images with limited computational costs. We find existing frequency-based SR methods cannot balance the reconstruction of overall structures and high-frequency parts. Meanwhile, these methods are inefficient for handling frequency features and unsuitable for lightweight SR. In this paper, we show introducing both wavelet and Fourier information allows our model to consider both high-frequency features and overall SR structure reconstruction while reducing costs. Specifically, we propose a dual-domain modulation network that utilize wavelet-domain modulation self-Transformer (WMT) plus Fourier supervision to modulate frequency features in addition to spatial domain modulation. Compared to existing frequency-based SR modules, our WMT is more suitable for frequency learning in lightweight SR. Experimental results show that our method achieves a comparable PSNR of SRFormer and MambaIR while with less than 50% and 60% of their FLOPs and achieving inference speeds 15.4x and 5.4x faster, respectively, demonstrating the effectiveness of our method on SR quality and lightweight. Codes will be released upon acceptance.