Spread Your Wings: A Radial Strip Transformer for Image Deblurring
<|reference_start|>Spread Your Wings: A Radial Strip Transformer for Image Deblurring: 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.<|reference_end|>
@article{chen2024spread,
    title={Spread Your Wings: A Radial Strip Transformer for Image Deblurring},
    author={Duosheng Chen, Shihao Zhou, Jinshan Pan, Jinglei Shi, Lishen Qu and
  Jufeng Yang},
    journal={arXiv preprint arXiv:2404.00358},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00358},
    primaryClass={cs.CV}
}