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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