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Color computational ghost imaging based on a plug-and-play generalized alternating projection.

Shoupei Liu, Qi Li, Huazheng Wu

|October 12, 2022

Summary

A new computational ghost imaging (CGI) method reconstructs high-fidelity color images efficiently. This technique uses a plug-and-play generalized alternating projection algorithm (PnP-GAP) with a deep denoising network for improved results.

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Area of Science:

Optics and Photonics
Computational Imaging
Computer Vision

Background:

Computational ghost imaging (CGI) offers advantages for image retrieval using speckle patterns.
High-quality, time-efficient color CGI remains a significant challenge.
Existing methods struggle with balancing resolution, speed, and color fidelity.

Purpose of the Study:

To introduce a novel color CGI method for high-fidelity image reconstruction.
To reduce the sampling rate required for effective color imaging.
To enhance the practical applicability of CGI in real-world scenarios.

Main Methods:

Developed a new color CGI approach combining speckle patterns and a Bayer color mask.
Employed a plug-and-play generalized alternating projection algorithm (PnP-GAP).
Integrated a pre-trained deep denoising network and a joint reconstruction-demosaicking technique.

Main Results:

Achieved high-fidelity color image reconstruction at a low sampling rate (0.0625).
Demonstrated superior performance compared to classical reconstruction algorithms via simulations and experiments.
Successfully encoded spatial and color information into a single intensity sequence.

Conclusions:

The proposed color CGI scheme significantly improves imaging quality and efficiency.
The PnP-GAP algorithm with deep learning enhances reconstruction accuracy.
This method advances CGI for capturing detailed information in real scenes.