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The Application of Supervised Machine Learning Algorithms for Image Alignment in Multi-Channel Imaging Systems.

Kyrylo Romanenko¹, Yevgen Oberemok^2,3, Ivan Syniavskyi^1,3

¹Department of Computer-Integrated Technologies of Device Production, Faculty of Instrumentation Engineering, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Beresteiskyi Ave., 37, 03056 Kyiv, Ukraine.

Sensors (Basel, Switzerland)

|January 25, 2025

Summary

This study introduces a novel method for precise image alignment in multi-channel imaging systems using machine learning and a calibration grid. The technique significantly reduces geometric parameter errors, enhancing image accuracy for scientific applications.

Keywords:

geometric calibration image alignment image analysis image processing machine learning algorithms multi-channel imaging systems

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Area of Science:

Optics and Photonics
Image Processing
Machine Learning Applications

Background:

Accurate geometric parameter alignment is crucial for multi-channel imaging systems.
Existing methods often lack precision or are computationally intensive.
Image distortions in polarimeters can lead to significant errors in data analysis.

Purpose of the Study:

To develop and validate a robust method for geometric image alignment in multi-channel imaging systems.
To improve the accuracy of image data acquired from multispectral imaging polarimeters.
To establish a repeatable geometric calibration process for imaging systems.

Main Methods:

Utilized a calibration setup with an array of markers on a grid.
Employed machine learning algorithms (multiple polynomial regression) to model geometric displacements.
Established one imaging channel as a reference for alignment.
Applied pre-processing techniques to marker images.

Main Results:

Achieved significant reduction in standard image alignment error from 4.8 to 0.5 pixels in polarimeter channels.
Developed correction models capable of aligning geometric parameters across different imaging channels.
Demonstrated the effectiveness of the method in a multispectral imaging polarimeter module.

Conclusions:

The proposed method provides an effective and accurate solution for geometric calibration of multi-channel imaging systems.
The machine learning-based approach offers a repeatable and efficient way to correct image distortions.
This advancement is critical for improving the reliability and precision of data from advanced imaging instruments.