Researchers from The University of Hong Kong, Northwestern Polytechnical University, The Chinese University of Hong Kong, Guangdong University of Technology, and Massachusetts Institute of Technology reviewed the use of deep learning for phase recovery in a recent paper published in Light: Science & Applications. This approach is crucial for obtaining the structure of the samples.
Traditionally, there were several methods for phase recovery, but they faced challenges such as low spatiotemporal resolution and high computational complexity. The researchers explored four perspectives of deep learning for phase recovery. In the first perspective, they used deep learning to pre-process intensity measurements for better phase recovery results. The second perspective involved implementing deep learning during the phase recovery process for faster and more accurate results. In the third perspective, researchers focused on using deep learning for post-processing after phase recovery. Finally, in the fourth perspective, they explored using the recovered phase for specific applications, such as segmentation, classification, and imaging modality transformation.
While using deep learning for phase recovery offers many benefits, it also has limitations and risks. The researchers recommend combining physical models with deep neural networks to overcome these challenges and increase the overall accuracy of the method.
This technique has significant advantages over traditional methods, including enhanced speed, accuracy, and versatility, but ongoing research is required to address any limitations. The potential of deep learning for phase recovery is promising and has the potential to advance the understanding of complex systems in diverse fields.
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