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Speckle autocorrelation separation for multi-target scattering imaging.

Dajiang Lu, Yuliu Feng, Xiang Peng

|February 24, 2023

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This study presents a new method for imaging through scattering media using deep learning to improve the speckle autocorrelation technique (SAT). The approach successfully recovers multiple isolated targets, overcoming limitations of traditional methods.

Area of Science:

Optics and Photonics
Computational Imaging
Machine Learning Applications

Background:

Imaging through scattering media is a significant challenge in optical science.
The speckle autocorrelation technique (SAT) offers a promising non-invasive approach but is limited by the optical memory effect (OME) range.
Traditional SAT struggles with large-scale or multiple isolated targets.

Purpose of the Study:

To develop a novel multi-target scattering imaging scheme.
To overcome the limitations of the traditional speckle autocorrelation technique (SAT) regarding target scale and number.
To enhance imaging capabilities through scattering media by integrating deep learning.

Main Methods:

A hybrid approach combining the traditional speckle autocorrelation algorithm (SA) with a Deep Learning (DL) strategy.

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Utilizing a DL method to extract individual autocorrelation components from mixed speckle data.

Applying a phase retrieval algorithm (PRA) to reconstruct target shapes from extracted components.

Main Results:

Successfully demonstrated a multi-target scattering imaging scheme.
The proposed method effectively extracts autocorrelation components of individual targets from mixed speckle.
Experimental results show the successful recovery of up to five isolated targets.
Overcame the limitations of traditional SAT in imaging multiple small targets.

Conclusions:

The integration of Deep Learning with speckle autocorrelation significantly advances scattering imaging capabilities.
This novel scheme enables the non-invasive recovery of multiple isolated targets, expanding the applicability of SAT.
The developed method provides a robust solution for complex scattering imaging scenarios.