Physics-Driven Machine Learning for Computational Imaging

doi:10.1109/MSP.2022.3222888

Physics-Driven Machine Learning for Computational Imaging

2023-01-01

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Bihan Wen *

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Saiprasad Ravishankar

DOI：10.1109/MSP.2022.3222888

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Abstract

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Recent years have witnessed a rapidly growing interest in next-generation imaging systems and their combination with machine learning. While model-based imaging schemes that incorporate physics-based forward models, noise models, and image priors laid the foundation in the emerging field of computational sensing and imaging, recent advances in machine learning, from large-scale optimization to building deep neural networks, are increasingly being applied in modern computational imaging. A wide range of machine learning techniques can be applied to enhance the effectiveness and efficiency of computational imaging systems, thus redefining state-of-the-art computational imaging algorithms.

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Machine learning

Computational modeling

Image processing

Physics-Driven Machine Learning for Computational Imaging

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