@inproceedings{
mao2024loformer,
title={LoFormer: Local Frequency Transformer for Image Deblurring},
author={Xintian Mao and Jiansheng Wang and Xingran Xie and Qingli Li and Yan Wang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=57cL5TwLAH}
}
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. Code will be released.
LoFormer: Local Frequency Transformer for Image Deblurring
Xintian Mao Jiansheng Wang Xingran Xie Qingli Li Yan Wang