Harnessing Data and Physics for Deep Learning Phase Recovery
Kaiqiang Wang Edmund Y. Lam
Phase recovery, calculating the phase of a light wave from its intensity
measurements, is essential for various applications, such as coherent
diffraction imaging, adaptive optics, and biomedical imaging. It enables the
reconstruction of an object's refractive index distribution or topography as
well as the correction of imaging system aberrations. In recent years, deep
learning has been proven to be highly effective in addressing phase recovery
problems. Two main deep learning phase recovery strategies are data-driven (DD)
with supervised learning mode and physics-driven (PD) with self-supervised
learning mode. DD and PD achieve the same goal in different ways and lack the
necessary study to reveal similarities and differences. Therefore, in this
paper, we comprehensively compare these two deep learning phase recovery
strategies in terms of time consumption, accuracy, generalization ability,
ill-posedness adaptability, and prior capacity. What's more, we propose a
co-driven (CD) strategy of combining datasets and physics for the balance of
high- and low-frequency information. The codes for DD, PD, and CD are publicly
available at https://github.com/kqwang/DLPR.