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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
Published on: February 12, 2014
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A diffusion-based super resolution model for enhancing sonar images
Oscar Bryan1,2, Thibaud Berthomier2, Benoit D'Ales2
1University of Bath, Bath, United Kingdom.
The Journal of the Acoustical Society of America
|January 23, 2025
Summary
This study uses a conditioned diffusion model to enhance low-resolution sonar images, improving machine learning compatibility and detection rates without increasing false positives.
Area of Science:
- Marine technology
- Artificial intelligence
- Image processing
Background:
- Modern sonar systems achieve centimeter resolution, but legacy datasets are limited.
- Practical constraints and older systems hinder resolution in existing sonar data.
- A unified dataset is needed for advanced machine learning applications.
Purpose of the Study:
- To upscale low-resolution sonar datasets using advanced AI.
- To ensure backward compatibility between legacy and high-resolution sonar data.
- To improve machine learning performance on unified sonar datasets.
Main Methods:
- Employed a single image super-resolution technique utilizing a conditioned diffusion model.
- Developed a mapping approach to upscale low-resolution sonar images to higher resolutions.
- Introduced two novel sonar-specific evaluation metrics grounded in acoustic physics and ATR utility.
Main Results:
- Upscaled images showed improved classification performance.
- The diffusion model approach did not increase the probability of false detections.
- Achieved a 7% higher probability of detection than bicubic interpolation, 6% over CNNs, and 2% over GANs.
Conclusions:
- Conditioned diffusion models effectively upscale sonar imagery for enhanced machine learning.
- The proposed method bridges resolution gaps in sonar datasets, enabling unified analysis.
- Novel metrics provide better evaluation of sonar image enhancement for target recognition.
