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

Area of Science:

• Biomedical Optics
• Computational Imaging
• Fiber Optics

Background:

• Compressive multimode fiber (MMF) endoscopic imaging offers high resolution in thin endoscopes.
• Motion during imaging degrades image quality, limiting practical applications.
• Existing methods struggle with dynamic imaging conditions.

Purpose of the Study:

• To develop a method for mitigating motion blur in compressive MMF endoscopic imaging.
• To enable high-quality imaging under realistic, dynamic operational conditions.
• To improve the applicability of MMF imaging in scenarios with probe or object movement.

Main Methods:

• Developed a dynamic imaging model for translational and rotational motions.
• Combined compressed sensing with Kalman filtering for image reconstruction.
• Utilized a sparse Kalman filtering algorithm for dynamic tracking of sparse signals.

Main Results:

• Successfully enhanced image quality degraded by motion interference.
• Demonstrated robust resistance to noise in simulations.
• Validated the method's effectiveness in realistic imaging scenarios.
• Showcased the ability to dynamically expand the field of view.

Conclusions:

• The proposed approach effectively overcomes motion blur limitations in compressive MMF imaging.
• This technique significantly improves image quality and robustness in dynamic environments.
• It presents a promising solution for high-quality endoscopic imaging under practical conditions.