Robust compressed ghost imaging against environmental influence factors.

Area of Science:

• Optics and Photonics
• Computational Imaging
• Signal Processing

Background:

• Sparse sampling ghost imaging is susceptible to environmental factors like instrumental drift and ambient light.
• These factors degrade image quality in practical ghost imaging applications.
• Conventional compressed sensing methods struggle to mitigate these real-world measurement errors.

Purpose of the Study:

• To develop a robust compressed sensing technique to reduce the influence of measurement errors on image quality.
• To implement and validate this technique in the context of ghost imaging.
• To demonstrate its effectiveness against environmental influences.

Main Methods:

• The proposed technique is differential compressed sensing ghost imaging (DCSGI).
• DCSGI utilizes differential measurements to eliminate error terms.
• Higher-order DCSGI (n-order) eliminates the first n Taylor expansion polynomials of the error.

Main Results:

• DCSGI was theoretically and experimentally verified to work well with typical errors in ghost imaging.
• The proposed DCSGI significantly outperforms conventional approaches in the presence of environmental errors.
• The technique effectively reduces degradation of image quality caused by instrumental drift and ambient light changes.

Conclusions:

• Differential compressed sensing ghost imaging (DCSGI) offers a robust solution for high-quality image reconstruction.
• The technique effectively mitigates common environmental interference in ghost imaging.
• DCSGI shows potential for broader applications in compressed sensing requiring anti-interference capabilities.