Rotation center identification based on geometric relationships for rotary motion deblurring

TL;DR

This paper introduces a geometric method for accurately identifying the rotation center in rotary motion deblurring, leveraging fixed system priors to improve estimation accuracy and enhance deblurring performance.

Contribution

The paper proposes a novel geometric-based approach for rotation center identification that reduces estimation errors by utilizing prior knowledge of fixed rotation centers in imaging systems.

Findings

Achieves less than 1-pixel error in rotation center estimation.

Improves the quality of deblurred images when integrated with existing RMD methods.

Validates the approach with real RMB images captured from a constructed imaging system.

Abstract

Non-blind rotary motion deblurring (RMD) aims to recover the latent clear image from a rotary motion blurred (RMB) image. The rotation center is a crucial input parameter in non-blind RMD methods. Existing methods directly estimate the rotation center from the RMB image. However they always suffer significant errors, and the performance of RMD is limited. For the assembled imaging systems, the position of the rotation center remains fixed. Leveraging this prior knowledge, we propose a geometric-based method for rotation center identification and analyze its error range. Furthermore, we construct a RMB imaging system. The experiment demonstrates that our method achieves less than 1-pixel error along a single axis (x-axis or y-axis). We utilize the constructed imaging system to capture real RMB images, and experimental results show that our method can help existing RMD approaches yield better RMD images.