Motion deblurring and quality enhancement for compressive multimode fiber imaging using sparse Kalman filtering

Compressive multimode fiber (MMF) endoscopic imaging represents a promising technology that enables ultra-thin dimensions and ultra-high resolution with a reduced sampling rate. However, its dependence on static imaging conditions for the time-sequential measurements significantly constrains its applicability, as fiber probes and objects typically experience motion in practical operational scenarios. In this study, we present a approach for compressive MMF imaging aimed at mitigating motion blur. The proposed method begins by establishing a model for dynamic imaging objects undergoing translational or rotational motions. It then combines compressed sensing theory with Kalman filtering techniques to transform image reconstruction into the dynamic tracking of time-varying sparse signals, achieving an optimal solution through a sparse Kalman filtering algorithm. Extensive numerical simulations confirm that our approach effectively enhances image quality degraded by motion interference while exhibiting robust resistance to noise. Furthermore, this method allows for dynamically expanding the field of view, positioning it as a promising tool for achieving high-quality compressive MMF endoscopic imaging under realistic operating conditions.