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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Optimization of Imaging Reconnaissance Systems Using Super-Resolution: Efficiency Analysis in Interference

Marta Bistroń¹, Zbigniew Piotrowski¹

¹Institute of Communication Systems, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, Poland.

Sensors (Basel, Switzerland)

|January 8, 2025

Summary

Super-resolution (SR) technology, like Real-ESRGAN, enhances image quality. This improves object detection accuracy in challenging conditions, boosting system performance.

Keywords:

Real-ESRGAN faster R-CNN imaging reconnaissance object detection super-resolution

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Image reconnaissance systems are vital for object detection and identification.
Real-world image distortions (motion blur, noise, compression artifacts) degrade system performance.
Super-resolution (SR) technology offers potential solutions for image quality enhancement.

Purpose of the Study:

To analyze the impact of SR technology on object detection model performance under disturbed conditions.
To evaluate the effectiveness of the Real-ESRGAN model in improving detection accuracy.
To identify areas for future development in SR-enhanced imaging systems.

Main Methods:

Trained and evaluated the Faster R-CNN detection model.
Utilized original and modified datasets with various image distortions.
Assessed detection precision and mean Average Precision (mAP) as key metrics.

Main Results:

SR significantly improved detection precision across most interference scenarios.
The Real-ESRGAN model demonstrated effectiveness in enhancing performance under degraded image quality.
mAP showed notable improvements, indicating better overall detection capabilities.

Conclusions:

SR technology, particularly Real-ESRGAN, substantially enhances object detection performance in disturbed conditions.
SR is a promising approach for improving the robustness of imaging and reconnaissance systems.
Further research is needed to optimize SR models and explore broader applications.