An End-to-End Network for Rotary Motion Deblurring in the Polar Coordinate System

Non-blind rotary motion deblurring (RMD) aims to restore a latent image from its blurred image. Since the integration path of rotary motion blurring (RMB) is a circle, RMD is modelled as a typical motion deblurring in the polar coordinate system (PCS). However, existing PCS-based methods use hand-designed image priors and are limited by transformation errors, including Cartesian-to-polar transformation (CPT) error and polar-to-Cartesian transformation (PCT) error. In this paper, we analyze the impact of transformation errors on the restored image and propose a novel end-to-end network which introduces a convolutional neural network (CNN) to learn image priors. Specifically, considering the CPT error, we construct a degradation model and solve it in an unrolling way, effectively reducing the ringing artifacts. For the PCT error, we develop a PCT error correction module (PCM) to reconstruct the lost details and textures. Experiments show our method performs against state-of-the-art (SOTA) approaches on synthetic and real-world rotary motion blur datasets by a large margin. The code and model are available at https://github.com/Jinhui-Qin/RMD_PCS.