  Exploring motion information is important for the motion deblurring task.
Recent the window-based transformer approaches have achieved decent performance
in image deblurring. Note that the motion causing blurry results is usually
composed of translation and rotation movements and the window-shift operation
in the Cartesian coordinate system by the window-based transformer approaches
only directly explores translation motion in orthogonal directions. Thus, these
methods have the limitation of modeling the rotation part. To alleviate this
problem, we introduce the polar coordinate-based transformer, which has the
angles and distance to explore rotation motion and translation information
together. In this paper, we propose a Radial Strip Transformer (RST), which is
a transformer-based architecture that restores the blur images in a polar
coordinate system instead of a Cartesian one. RST contains a dynamic radial
embedding module (DRE) to extract the shallow feature by a radial deformable
convolution. We design a polar mask layer to generate the offsets for the
deformable convolution, which can reshape the convolution kernel along the
radius to better capture the rotation motion information. Furthermore, we
proposed a radial strip attention solver (RSAS) as deep feature extraction,
where the relationship of windows is organized by azimuth and radius. This
attention module contains radial strip windows to reweight image features in
the polar coordinate, which preserves more useful information in rotation and
translation motion together for better recovering the sharp images.
Experimental results on six synthesis and real-world datasets prove that our
method performs favorably against other SOTA methods for the image deblurring
task.
