TL;DR:
- Traditional optical detectors struggle with phase recovery due to limited sampling frequency.
- Four perspectives on utilizing deep learning for phase recovery: pre-processing, in-process integration, post-processing, and applications.
- Deep learning enhances phase recovery speed and accuracy, but subtle method differences pose challenges.
- A hybrid approach combining deep learning and physical models can mitigate risks and improve accuracy.
Main AI News:
In the realm of optical sciences, the understanding of light has always revolved around its two fundamental aspects: amplitude and phase. Nonetheless, conventional optical detectors, reliant on photon-to-electron conversion, encounter a formidable challenge in accurately capturing the phase component due to the constraints of their sampling frequency. While adept at measuring amplitude, their proficiency in the grasping phase remains limited, and this limitation poses a significant hurdle. The phase of the light field carries invaluable information crucial for discerning the intricate structures within samples.
Traditionally, researchers employed various conventional techniques for phase recovery, including holography/interferometry, Shack-Hartmann wavefront sensing, transport of intensity equation, and optimization-based approaches. Although these methods have proven useful, each harbored its set of limitations, characterized by low spatiotemporal resolution and high computational complexity.
In a recent groundbreaking review paper published in Light: Science & Applications, 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 have explored the potential of deep learning in the realm of phase recovery from four distinct perspectives.
Firstly, deep learning is harnessed for pre-processing intensity measurements preceding phase recovery. Techniques such as pixel super-resolution, noise reduction, hologram generation, and autofocusing serve to enhance the quality of input data, ultimately leading to improved phase recovery outcomes.
In the second perspective, researchers delve into the integration of deep learning within the phase recovery process itself. Neural networks are employed independently or in conjunction with physical models to expedite and enhance phase recovery. This approach presents the advantage of yielding faster and more accurate results compared to traditional methods.
The third perspective revolves around deep learning for post-processing following phase recovery. This facet involves noise reduction, resolution enhancement, aberration correction, and phase unwrapping techniques, all contributing to refining the accuracy of the recovered phase.
Finally, the fourth perspective explores the utilization of the retrieved phase for specific applications such as segmentation, classification, and imaging modality transformation. This utilization facilitates the extraction of valuable insights regarding the properties and behavior of the investigated samples from the recovered phase data.
While the merits of employing deep learning for phase recovery are undeniable, the researchers acknowledge the existence of certain limitations and associated risks. They stress the subtle but critical differences among various methods, which can be challenging to discern. To mitigate these risks, they propose a hybrid approach that combines physical models with deep neural networks, particularly when the physical model closely aligns with the underlying reality. This strategic fusion enhances the overall accuracy and reliability of the phase recovery method.
Conclusion:
The integration of deep learning techniques into the domain of phase recovery holds the promise of revolutionizing computational imaging. This comprehensive analysis not only sheds light on the potential benefits but also underscores the need for a balanced approach that harnesses the strengths of both deep learning and traditional methods to unlock new horizons in optical sciences and imaging technologies.
