Physics-Informed Machine Learning for Computational Imaging

Kristina Monakhova

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2022-177
July 29, 2022

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-177.pdf

A key aspect of many computational imaging systems, from compressive cameras to low light photography, are the algorithms used to uncover the signal from encoded or noisy measurements. Some computational cameras encode higher-dimensional information (e.g. different wavelengths of light, 3D, time) onto a 2-dimensional sensor, then use algorithms to decode and recover this high-dimensional information. Others capture measurements that are extremely noisy, or degraded, and require algorithms to extract the signal and make the images usable by people, or by higher-level downstream algorithms. In each case, the algorithms used to decode and extract information from raw measurements are critical and necessary to make computational cameras function. Over the years the predominant methods, classic methods, to recover information from computational cameras have been based on minimizing an optimization problem consisting of a data term and hand-picked prior term. More recently, deep learning has been applied to these problems, but often has no way to incorporate known optical characteristics, requires large training datasets, and results in black-box models that cannot easily be interpreted. In this dissertation, we present physics-informed machine learning for computational imaging, which is a middle ground approach that combines elements of classic methods with deep learning. We show how to incorporate knowledge of the imaging system physics into neural networks to improve image quality and performance beyond what is feasible with either classic or deep methods for several computational cameras. We show several different ways to incorporate imaging physics into neural networks, including algorithm unrolling, differentiable optical models, unsupervised methods, and through generative adversarial networks. For each of these methods, we focus on a different computational camera with unique challenges and modeling considerations. First, we introduce an unrolled, physics-informed network that improves the quality and reconstruction time of lensless cameras, improving these cameras and showing photorealistic image quality on a variety of scenes. Building up on this, we demonstrate a new reconstruction network that can improve the reconstruction time for compressive, single-shot 3D microscopy with spatially-varying blur by 1,600X, enabling interactive previewing of the scene. In cases where training data is hard to acquire, we show that an untrained physics-informed network can improve image quality for compressive single-shot video and hyperspectral imaging without the need for training data. Finally, we design a physics-informed noise generator that can realistically synthesize noise at extremely high-gain, low-light settings. Using this learned noise model, we show how we can push a camera past its typical limit and take photorealistic videos at starlight levels of illumination for the first time. Each case highlights how using physics-informed machine learning can improve computational cameras and push them to their limits.

Advisor: Laura Waller


BibTeX citation:

@phdthesis{Monakhova:EECS-2022-177,
    Author = {Monakhova, Kristina},
    Title = {Physics-Informed Machine Learning for Computational Imaging},
    School = {EECS Department, University of California, Berkeley},
    Year = {2022},
    Month = {Jul},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-177.html},
    Number = {UCB/EECS-2022-177},
    Abstract = {A key aspect of many computational imaging systems, from compressive cameras to low light photography, are the algorithms used to uncover the signal from encoded or noisy measurements. Some computational cameras encode higher-dimensional information (e.g. different wavelengths of light, 3D, time) onto a 2-dimensional sensor, then use algorithms to decode and recover this high-dimensional information. Others capture measurements that are extremely noisy, or degraded, and require algorithms to extract the signal and make the images usable by people, or by higher-level downstream algorithms. In each case, the algorithms used to decode and extract information from raw measurements are critical and necessary to make computational cameras function. Over the years the predominant methods, classic methods, to recover information from computational cameras have been based on minimizing an optimization problem consisting of a data term and hand-picked prior term. More recently, deep learning has been applied to these problems, but often has no way to incorporate known optical characteristics, requires large training datasets, and results in black-box models that cannot easily be interpreted. In this dissertation, we present physics-informed machine learning for computational imaging, which is a middle ground approach that combines elements of classic methods with deep learning. We show how to incorporate knowledge of the imaging system physics into neural networks to improve image quality and performance beyond what is feasible with either classic or deep methods for several computational cameras. We show several different ways to incorporate imaging physics into neural networks, including algorithm unrolling, differentiable optical models, unsupervised methods, and through generative adversarial networks. For each of these methods, we focus on a different computational camera with unique challenges and modeling considerations. First, we introduce an unrolled, physics-informed network that improves the quality and reconstruction time of lensless cameras, improving these cameras and showing photorealistic image quality on a variety of scenes. Building up on this, we demonstrate a new reconstruction network that can improve the reconstruction time for compressive, single-shot 3D microscopy with spatially-varying blur by 1,600X, enabling interactive previewing of the scene. In cases where training data is hard to acquire, we show that an untrained physics-informed network can improve image quality for compressive single-shot video and hyperspectral imaging without the need for training data. Finally, we design a physics-informed noise generator that can realistically synthesize noise at extremely high-gain, low-light settings. Using this learned noise model, we show how we can push a camera past its typical limit and take photorealistic videos at starlight levels of illumination for the first time. Each case highlights how using physics-informed machine learning can improve computational cameras and push them to their limits.}
}

EndNote citation:

%0 Thesis
%A Monakhova, Kristina
%T Physics-Informed Machine Learning for Computational Imaging
%I EECS Department, University of California, Berkeley
%D 2022
%8 July 29
%@ UCB/EECS-2022-177
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-177.html
%F Monakhova:EECS-2022-177