Stereo Image Super-Resolution (stereoSR) has attracted significant attention
in recent years due to the extensive deployment of dual cameras in mobile
phones, autonomous vehicles and robots. In this work, we propose a new StereoSR
method, named SwinFSR, based on an extension of SwinIR, originally designed for
single image restoration, and the frequency domain knowledge obtained by the
Fast Fourier Convolution (FFC). Specifically, to effectively gather global
information, we modify the Residual Swin Transformer blocks (RSTBs) in SwinIR
by explicitly incorporating the frequency domain knowledge using the FFC and
employing the resulting residual Swin Fourier Transformer blocks (RSFTBs) for
feature extraction. Besides, for the efficient and accurate fusion of stereo
views, we propose a new cross-attention module referred to as RCAM, which
achieves highly competitive performance while requiring less computational cost
than the state-of-the-art cross-attention modules. Extensive experimental
results and ablation studies demonstrate the effectiveness and efficiency of
our proposed SwinFSR.

通过使用 Fast Fourier Convolution 和 Residual Swin Transformer 改进的方法和新的 cross-attention 模块，我们提出了一种名为 SwinFSR 的 Stereo Image Super-Resolution 方法，并且实验结果表明了该方法的有效性和高效性。