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This study introduces a self-calibrating single fiber scanner (SFS) using piezoelectric sensors and machine learning to correct image distortions. The novel approach significantly enhances imaging quality and stability in complex environments.

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Area of Science:

Optics and Photonics
Sensor Technology
Machine Learning Applications

Background:

Single fiber scanners (SFSs) offer advantages like compact size and high resolution but suffer from image distortions.
Existing methods to correct these distortions have limitations, hindering SFS performance.

Purpose of the Study:

To develop a simple and complete solution for correcting image distortions in SFSs.
To improve the imaging quality, performance, and stability of SFSs in diverse conditions.

Main Methods:

Utilized a piezoelectric (PZT) tube as both an actuator and a fiber position sensor for self-induction.
Employed a convolution neural network to process sensor signals and eliminate noise for accurate fiber trajectory prediction.

Main Results:

Achieved a predicted fiber trajectory error below 0.1%.
Demonstrated excellent robustness to temperature variations (20-50°C).

Conclusions:

The proposed self-calibration method effectively removes major barriers to SFS performance.
This innovation significantly enhances the stability and reliability of SFS in complex environments.