Physics-Driven Machine Learning for Computational Imaging

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.

Automated summary

In recent years, there has been a growing interest in next-generation imaging systems and their combination with machine learning. Model-based imaging schemes, which incorporate physics-based forward models, noise models, and image priors, have laid the foundation in the emerging field of computational sensing and imaging. However, recent advances in machine learning techniques, such as large-scale optimization and deep neural networks, are increasingly being applied to improve the effectiveness and efficiency of computational imaging systems, leading to the redefinition of state-of-the-art computational imaging algorithms.