  Currently, transformer-based algorithms are making a splash in the domain of
image deblurring. Their achievement depends on the self-attention mechanism
with CNN stem to model long range dependencies between tokens. Unfortunately,
this ear-pleasing pipeline introduces high computational complexity and makes
it difficult to run an ultra-high-definition image on a single GPU in real
time. To trade-off accuracy and efficiency, the input degraded image is
computed cyclically over three dimensional ($C$, $W$, and $H$) signals without
a self-attention mechanism. We term this deep network as Multi-scale
Cubic-Mixer, which is acted on both the real and imaginary components after
fast Fourier transform to estimate the Fourier coefficients and thus obtain a
deblurred image. Furthermore, we combine the multi-scale cubic-mixer with a
slicing strategy to generate high-quality results at a much lower computational
cost. Experimental results demonstrate that the proposed algorithm performs
favorably against the state-of-the-art deblurring approaches on the several
benchmarks and a new ultra-high-definition dataset in terms of accuracy and
speed.
