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Low sampling high quality image reconstruction and segmentation based on array network ghost imaging.

Xuan Liu, Tailin Han, Cheng Zhou

|May 9, 2023

Summary

This summary is machine-generated.

This study introduces a new deep learning method for computational ghost imaging (CGI) using array detectors. It achieves high-quality image reconstruction and segmentation simultaneously, even with low sampling rates.

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Area of Science:

Computational imaging
Deep learning applications
Optical sensing

Background:

Computational ghost imaging (CGI) requires high-quality imaging under low sampling times for practical use.
Current deep learning approaches for CGI primarily focus on single-pixel detection, limiting imaging performance.
The integration of array detection with deep learning for enhanced CGI performance remains underexplored.

Purpose of the Study:

To develop a novel multi-task computational ghost imaging (CGI) detection method utilizing deep learning and an array detector.
To enable simultaneous high-quality image reconstruction and image-free segmentation from low-sampling-rate, one-dimensional bucket detection signals.
To address challenges in CGI, including partial information loss and improving imaging efficiency.

Main Methods:

A multi-task deep learning framework was designed for array detector-based CGI.
The method extracts target features directly from one-dimensional bucket signals at low sampling times.
Light field modulation efficiency was enhanced by binarizing spatial light fields and fine-tuning the network, and reconstruction issues due to detector gaps were resolved.

Main Results:

The proposed method achieved simultaneous high-quality reconstructed and segmented images at a sampling rate of 0.78%.
Clear image details were maintained even at a signal-to-noise ratio of 15 dB for bucket signals.
The method effectively solved the problem of partial information loss caused by array detector gaps.

Conclusions:

This novel deep learning approach significantly enhances the capabilities of computational ghost imaging (CGI) with array detectors.
The method offers simultaneous high-quality reconstruction and segmentation, improving applicability in resource-constrained, multi-task detection scenarios.
Potential applications include real-time detection, semantic segmentation, and object recognition.