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LoFormer: Local Frequency Transformer for Image Deblurring by Mao, Xintian; Wang, Jiansheng; Xie, Xingran; Li, Qingli; Wang, Yan is a scholarly article available to read on EtoBox.

What is LoFormer: Local Frequency Transformer for Image Deblurring about?

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 th

Author

Mao, Xintian; Wang, Jiansheng; Xie, Xingran; Li, Qingli; Wang, Yan

Published

2024

Language

EN