Rotation center identification based on geometric relationships for rotary motion deblurring

TL;DR

This paper tackles rotation center identification for RMB images, a key input for non-blind rotary motion deblurring. It introduces a geometry-based identification method tailored to assembled RMB imaging systems and provides a per-axis error bound of $<0.5$ pixels, validated experimentally with a constructed RMB imaging system showing practical per-axis errors under $1$ pixel. The authors demonstrate that accurate rotation center estimation improves non-blind RMD results, outperforming conventional algorithm-based estimations that introduce ringing artifacts. The work offers a practical solution to enhance RMB deblurring in aerospace and related applications by fixing the center position and enabling more reliable RMD performance.

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.

Figures (9)

• Figure 1: The RMD examples with different rotation center errors $\delta p$ along the x-axis. The blurred image is degraded by additive white Gaussian noise with strength $\sigma=1\%$.
• Figure 2: The position of the rotation center remains fixed under different rotation angles.
• Figure 3: The identification process along the y-axis. The tangent area is zoomed in, as shown in the yellow box of (a).
• Figure 4: The diagram of rotation center error analysis.
• Figure 5: The top view of the RMB imaging system.