Physics-guided deep unfolding network for snapshot 3D imaging using double-helix point spread function

Abstract

Point spread function (PSF) engineering is a promising approach for passive, snapshot 3D imaging with a single detector. A widely used technique is the double-helix PSF (DH-PSF), which employs a specialized phase mask at the pupil plane to modulate incident light, generating rotationally varying PSFs with defocus. By leveraging a precalibrated depth-dependent PSF model, the depth information of the target surface can be recovered from a snapshot measurement. However, existing reconstruction algorithms often lack efficiency and accuracy, primarily due to the block-wise processing of conventional methods or the failure to incorporate physical priors in end-to-end neural networks. To address these limitations, we propose a physics-guided deep unfolding network (PG-DUN) for snapshot 3D imaging with DH-PSFs. By explicitly embedding the imaging model into the deep neural network, our DUN can naturally reconstruct the 2D image and depth map simultaneously, contributing to more accurate and efficient reconstruction than previous approaches. The feasibility and effectiveness of the proposed method are validated through extensive experiments on simulated and real-world data. The proposed method can serve as a prototype for a deep learning-based reconstruction model in similar deconvolution tasks. Its key innovation—an accelerated deconvolutional gradient descent design—functions as a plug-and-play component that enhances the reconstruction accuracy of any deep neural network with negligible added computational cost.