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Simulating the Mechanics of Lens Accommodation via a Manual Lens Stretcher

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Dual-constrained physics-enhanced untrained neural network for lensless imaging.

Zehua Wang, Shenghao Zheng, Zhihui Ding

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|March 4, 2024

Summary

This study introduces a dual-constrained untrained neural network (UNN) for lensless imaging. The novel method overcomes UNN overfitting, enabling stable and robust phase retrieval from intensity data.

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Area of Science:

Computational imaging
Applied physics
Machine learning

Background:

Lensless imaging offers advantages but requires accurate phase retrieval.
Untrained neural networks (UNNs) enable imaging from intensity data without large datasets.
UNNs face challenges with overfitting, hindering stable and robust reconstruction.

Purpose of the Study:

To develop a robust phase retrieval method for lensless imaging using UNNs.
To address the overfitting problem in UNNs for improved reconstruction stability.
To introduce a dual-constrained network framework for intensity-to-phase conversion.

Main Methods:

A dual-constrained untrained network was modeled for phase retrieval.
A phase-amplitude alternating optimization framework was designed.
Phase optimization incorporated deep image and total variation priors.
Amplitude optimization utilized a total variation denoising-based Wirtinger gradient descent method.

Main Results:

The proposed method effectively splits the intensity-to-phase problem into distinct optimization tasks.
Iterative optimization of phase and amplitude led to high-performance wavefield reconstruction.
Experimental results validated the superiority of the dual-constrained UNN approach.

Conclusions:

The dual-constrained UNN framework successfully mitigates overfitting in lensless imaging.
This approach provides a stable and robust solution for phase retrieval from single-frame intensity data.
The method demonstrates significant potential for advancing lensless imaging technologies.