Adaptive compressed sensing for the fast terahertz reflection tomography

Area of Science:

• Physics
• Electrical Engineering
• Image Processing

Background:

• Terahertz (T-ray) reflection tomography is a valuable imaging technique.
• Fast T-ray tomography can suffer from image degradation.
• Enhancing image quality in T-ray tomography is crucial for applications.

Purpose of the Study:

• To propose an adaptive compressed sensing method for fast terahertz reflection tomography.
• To improve the quality of reconstructed tomographic images.
• To validate the effectiveness of the proposed method compared to conventional approaches.

Main Methods:

• An adaptive compressed sensing approach was developed.
• Data acquisition was performed at random spatial points initially.
• Additional measurement points were adaptively allocated to regions with expected degradation.

Main Results:

• The adaptive method successfully identified and targeted regions prone to image degradation.
• More data were acquired in critical areas, leading to improved image quality.
• The proposed method demonstrated enhanced performance over conventional methods for the same number of measurement points.

Conclusions:

• Adaptive compressed sensing significantly enhances fast terahertz reflection tomography performance.
• The intelligent allocation of measurement points improves reconstructed image quality.
• This technique offers a viable solution for high-quality T-ray imaging.