  We present an effective and efficient method that explores the properties of
Transformers in the frequency domain for high-quality image deblurring. Our
method is motivated by the convolution theorem that the correlation or
convolution of two signals in the spatial domain is equivalent to an
element-wise product of them in the frequency domain. This inspires us to
develop an efficient frequency domain-based self-attention solver (FSAS) to
estimate the scaled dot-product attention by an element-wise product operation
instead of the matrix multiplication in the spatial domain. In addition, we
note that simply using the naive feed-forward network (FFN) in Transformers
does not generate good deblurred results. To overcome this problem, we propose
a simple yet effective discriminative frequency domain-based FFN (DFFN), where
we introduce a gated mechanism in the FFN based on the Joint Photographic
Experts Group (JPEG) compression algorithm to discriminatively determine which
low- and high-frequency information of the features should be preserved for
latent clear image restoration. We formulate the proposed FSAS and DFFN into an
asymmetrical network based on an encoder and decoder architecture, where the
FSAS is only used in the decoder module for better image deblurring.
Experimental results show that the proposed method performs favorably against
the state-of-the-art approaches. Code will be available at
\url{https://github.com/kkkls/FFTformer}.
