Deep learning has transformed computational imaging, but traditional pixel-based representations limit their ability to capture continuous multiscale object features. Addressing this gap, we introduce a local conditional neural field (LCNF) framework, which leverages a continuous neural representation to provide flexible object representations. LCNF’s unique capabilities are demonstrated in solving the highly ill-posed phase retrieval problem of multiplexed Fourier ptychographic microscopy. Our network, termed neural phase retrieval (NeuPh), enables continuous-domain resolution-enhanced phase reconstruction, offering scalability, robustness, accuracy, and generalizability that outperform existing methods. NeuPh integrates a local conditional neural representation and a coordinate-based training strategy. We show that NeuPh can accurately reconstruct high-resolution phase images from low-resolution intensity measurements. Furthermore, NeuPh consistently applies continuous object priors and effectively eliminates various phase artifacts, demonstrating robustness even when trained on imperfect datasets. Moreover, NeuPh improves accuracy and generalization compared with existing deep learning models. We further investigate a hybrid training strategy combining both experimental and simulated datasets, elucidating the impact of domain shift between experiment and simulation. Our work underscores the potential of the LCNF framework in solving complex large-scale inverse problems, opening up new possibilities for deep-learning-based imaging techniques.
KEYWORDS: Image restoration, Computational imaging, Deep learning, Education and training, Multiplexing, Inverse problems, Data modeling, Super resolution, Physics, Phase retrieval
Deep learning has revolutionized computational imaging, offering powerful solutions for performance enhancement and addressing diverse challenges. However, the traditional discrete pixel-based representations limit their ability to capture continuous, multiscale details of objects.
Here, we introduce a novel Local Conditional Neural Fields (LCNF) framework, leveraging a continuous implicit neural representation. We demonstrate the capabilities of LCNF in solving the highly ill-posed inverse problem in Fourier ptychographic microscopy (FPM) with multiplexed measurements. Our LCNF achieves versatile and generalizable continuous-domain super-resolution image reconstruction by combining a CNN-based encoder and an MLP-based decoder conditioned on a learned local latent vector. We show LCNF can accurately reconstruct wide field-of-view, high-resolution phase images, robustly capture the continuous object priors and eliminate various phase artifacts even trained imperfect datasets. We further demonstrate that LCNF exhibits strong generalization, reconstructing diverse biological samples with limited training data or dataset simulated using natural images.
3D particle-localization using in-line holography is a fundamental problem with important applications. It involves estimating the unknown positions of scatterers in a 3D volume from a single 2D hologram. We propose a deep learning based framework that is highly computationally efficient for large-scale 3D reconstructio and demonstrates accurate results for a wide variety of scattering scenarios.
The proposed approach incorporates physical scattering information into the result via 3D backpropagation of the hologram, followed by artifact removal with an end-to-end 3D deep neural network (DNN). To address the challenge of limited data availability, we train our DNN solely on simulated data, and show that it works accurately for experimental data as well. The results show that our DNN is able to accurately localize particles under various scattering scenarios with little computational overhead.
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