  Due to the computational complexity of self-attention (SA), prevalent
techniques for image deblurring often resort to either adopting localized SA or
employing coarse-grained global SA methods, both of which exhibit drawbacks
such as compromising global modeling or lacking fine-grained correlation. In
order to address this issue by effectively modeling long-range dependencies
without sacrificing fine-grained details, we introduce a novel approach termed
Local Frequency Transformer (LoFormer). Within each unit of LoFormer, we
incorporate a Local Channel-wise SA in the frequency domain (Freq-LC) to
simultaneously capture cross-covariance within low- and high-frequency local
windows. These operations offer the advantage of (1) ensuring equitable
learning opportunities for both coarse-grained structures and fine-grained
details, and (2) exploring a broader range of representational properties
compared to coarse-grained global SA methods. Additionally, we introduce an MLP
Gating mechanism complementary to Freq-LC, which serves to filter out
irrelevant features while enhancing global learning capabilities. Our
experiments demonstrate that LoFormer significantly improves performance in the
image deblurring task, achieving a PSNR of 34.09 dB on the GoPro dataset with
126G FLOPs. https://github.com/DeepMed-Lab-ECNU/Single-Image-Deblur
