Machine Learning Methods for Solving Optical Imaging Problems

Dear Colleagues,

Over the recent years, consistent efforts have been put into applying machine learning methods to address various problems in optical imaging. Across a growing number of optical imaging techniques, machine learning shows better performance over conventional methods. However, optical imaging spans a broad domain of machine learning methods in various fields, requiring ongoing explorations. Furthermore, the “data-driven” nature of deep learning approaches imposes limitations on their applicability, which calls for further attention. This Special Issue aims to highlight the potentials of machine learning methods across a spectrum of optical imaging techniques, including optical coherence tomography, photoacoustic imaging, optical spectroscopy, super-resolution microscopy and polarization imaging. Additionally, the objective is to investigate potential improvements of deep learning methods by leveraging prior knowledge of optical imaging systems, also known as physics-informed deep learning. Lastly, it aims to explore other emerging deep learning frameworks from the broader academic community, such as vision transformer, to provide additional solutions for optical imaging problems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

1. Deep learning for optical coherence tomography;
2. Deep learning for photoacoustic imaging;
3. Deep learning for optical spectroscopy;
4. Deep learning for super-resolution microscopy;
5. Physics-informed deep learning for optical imaging;
6. Advancing from convolutional neural network by investigating new deep learning architectures for optical imaging;
7. Advanced imaging technologies;
8. Computational imaging;
9. Polarization imaging;
10. Low-light imaging;
11. HDR imaging;
12. Hyperspectral imaging;
13. Infrared imaging and its applications.

We look forward to receiving your contributions.

Dr. Junchao Zhang
Dr. Xinglin Hou
Dr. Jianbo Shao
Dr. Yu Li
Guest Editors

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• optical imaging
• machine learning
• image processing