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Movement Artifact Direction Estimation Based on Signal Processing Analysis of Single-Frame Images
Woottichai Nonsakhoo1, Saiyan Saiyod1
1Hardware-Human Interface and Communications Laboratory (H2I-Comm Lab), Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen 40002, Thailand.
Sensors (Basel, Switzerland)
|December 31, 2025
Summary
This study introduces the Movement Artifact Direction Estimation (MADE) algorithm for analyzing single-frame images. MADE accurately estimates movement artifact direction and magnitude, crucial for noise detection in image analysis.
Area of Science:
- Image Processing
- Signal Processing
- Computational Imaging
Background:
- Movement artifacts are critical noise sources in single-frame images.
- Assessing artifact direction and magnitude is vital for accurate image analysis.
- Existing methods face computational challenges in medical image quality assessment.
Purpose of the Study:
- Introduce the Movement Artifact Direction Estimation (MADE) algorithm.
- Enable accurate estimation of movement artifact direction and magnitude in single-frame images.
- Address computational efficiency for real-time image quality assessment.
Main Methods:
- Developed a signal processing-based algorithm (MADE) using 3D geometric analysis.
- Utilized multi-directional quantification outputs (MAPE, ROPE, MAQ) from a preprocessing pipeline.
- Conducted experiments under controlled optical camera imaging conditions with precision apparatus.
Main Results:
- Demonstrated robust estimation of movement artifact direction (degrees) and magnitude (pixels).
- Achieved close alignment between estimated parameters and ground truth.
- Validated performance across various image shapes and velocities.
Conclusions:
- The MADE algorithm provides a methodological proof of concept for movement artifact analysis.
- Offers accurate directional and quantitative assessment of artifacts in single-frame images.
- Highlights potential for efficient, instantaneous image quality assessment systems.
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