Self-supervised learning of hologram reconstruction using physics consistency

Abstract

The past decade has witnessed transformative applications of deep learning in various computational imaging, sensing and microscopy tasks. Due to the supervised learning schemes employed, these methods mostly depend on large-scale, diverse, and labeled training data. The acquisition and preparation of such training image datasets are often laborious and costly, also leading to biased estimation and limited generalization to new sample types. Here, we report a self-supervised learning model, termed GedankenNet, that eliminates the need for labeled or experimental training data, and demonstrate its effectiveness and superior generalization on hologram reconstruction tasks. Without prior knowledge about the sample types to be imaged, the self-supervised learning model was trained using a physics-consistency loss and artificial random images that are synthetically generated without any experiments or resemblance to real-world samples. After its self-supervised training, GedankenNet successfully generalized to experimental holograms of various unseen biological samples, reconstructing the phase and amplitude images of different types of objects using experimentally acquired test holograms. Without access to experimental data or knowledge of real samples of interest or their spatial features, GedankenNet's self-supervised learning achieved complex-valued image reconstructions that are consistent with the Maxwell's equations, and its output inference and object solutions accurately represent the wave propagation in free-space. GedankenNet framework also exhibits resilience to random, unknown perturbations in the physical forward model, including changes in the hologram distances, pixel size and illumination wavelength. This self-supervised learning of image reconstruction tasks creates new opportunities for various inverse problems in holography, microscopy and computational imaging fields.

45 pages, 6 Figures

References in corpus (6)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Phase recovery and holographic image reconstruction using deep learning in neural networks
• Deep Learning Microscopy
• Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
• Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks
• eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging

Cited by in corpus (10)

• On the use of deep learning for phase recovery
• Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor
• All-optical complex field imaging using diffractive processors
• Deep learning phase recovery: data-driven, physics-driven, or combining both?
• eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging
• A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology
• Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Data
• Physics-guided hierarchical neural networks for Maxwell's equations in plasmonic metamaterials
• OAH-Net: A Deep Neural Network for Hologram Reconstruction of Off-axis Digital Holographic Microscope
• Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologram