Comprehensive Comparison of Data-Driven and Physics-Driven Deep Learning Strategies for Efficient Phase Recovery

Core Concepts

Data-driven and physics-driven deep learning strategies achieve the same goal of phase recovery from intensity measurements, but through different approaches. A comprehensive comparison reveals their similarities, differences, and trade-offs in terms of time consumption, accuracy, generalization ability, ill-posedness adaptability, and prior capacity.

Abstract

The content introduces two main deep learning strategies for phase recovery: data-driven (DD) and physics-driven (PD). DD trains neural networks with paired hologram-phase datasets as an implicit prior, while PD uses numerical propagation as an explicit prior to drive the training or inference of neural networks.
The paper compares these two strategies in detail:

Time consumption and accuracy: DD and tPD (trained PD) have quick inference times but lower accuracy, while uPD (untrained PD) and tPDr (tPD with refinement) achieve higher accuracy with longer inference times. To balance high- and low-frequency information, the authors propose a co-driven (CD) strategy that combines datasets and physics.

Generalization ability: The dataset is the main factor affecting the generalization ability of the trained neural network. tPD generalizes better than DD for inferring dense samples using neural networks trained on sparse samples.

Ill-posedness adaptability: For the case of inferring phase and amplitude simultaneously, DD can handle the ill-posedness better than tPD. The authors propose solutions for tPD by introducing aperture constraints or using multiple hologram inputs.

Prior capacity: DD can learn more about the implicit prior in the dataset, while PD can only learn the prior in numerical propagation, as demonstrated in the case of imaging aberration.

Finally, the authors verify the findings with experimental data, showing that uPD and tPDr have the highest accuracy, and CD balances high- and low-frequency information better than DD and tPD.

Stats

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Key Insights Distilled From

Harnessing Data and Physics for Deep Learning Phase Recovery

by Kaiqiang Wan... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01360.pdf

Deeper Inquiries

How can the data-driven and physics-driven strategies be further combined or integrated to leverage their respective strengths and achieve even better phase recovery performance

The data-driven (DD) and physics-driven (PD) strategies can be further combined or integrated to enhance phase recovery performance by leveraging their respective strengths. One approach could involve using the data-driven strategy to capture the low-frequency information in the phase recovery process, as it excels in learning from paired datasets and implicit priors. On the other hand, the physics-driven strategy can be utilized to handle high-frequency details and complex physical models that may be challenging for data-driven approaches alone.
By combining these strategies, a hybrid model can be developed that effectively balances the extraction of both high- and low-frequency information. This co-driven (CD) strategy can leverage the strengths of both approaches to achieve more accurate and robust phase recovery results. The data-driven component can provide a strong foundation based on empirical data, while the physics-driven component can enhance the model's ability to handle complex physical phenomena and improve generalization to new scenarios.

What are the potential limitations or challenges of the proposed co-driven strategy, and how can they be addressed

One potential limitation of the proposed co-driven strategy is the need for careful tuning of the balance between the data-driven and physics-driven components. Finding the optimal weighting or integration scheme for combining these two approaches may require extensive experimentation and validation. Additionally, the CD strategy may introduce additional complexity in the training process, as it involves integrating two distinct learning paradigms.
To address these challenges, thorough experimentation and validation on diverse datasets and scenarios are essential to fine-tune the CD strategy. Hyperparameter optimization, such as adjusting the weights assigned to the data-driven and physics-driven components, can help optimize the performance of the combined model. Furthermore, continuous monitoring and evaluation of the model's performance on various test cases can help identify and mitigate any limitations or challenges that arise during implementation.

Given the insights from this comparative analysis, how might the deep learning-based phase recovery techniques be applied or extended to other computational imaging and sensing modalities beyond just in-line holography

The insights gained from the comparative analysis of deep learning-based phase recovery techniques can be applied and extended to various other computational imaging and sensing modalities beyond in-line holography. For example:

Fourier Ptychography: The principles and strategies discussed can be adapted to improve phase retrieval in Fourier Ptychography, enabling more accurate and efficient reconstruction of complex wavefronts.
Tomographic Imaging: Deep learning-based phase recovery techniques can be extended to tomographic imaging applications, enhancing the reconstruction of 3D structures from multiple projections.
Interferometric Imaging: By incorporating data-driven and physics-driven approaches, interferometric imaging systems can benefit from improved phase retrieval capabilities, leading to enhanced resolution and accuracy in interferometric measurements.
By applying the lessons learned from phase recovery to these other modalities, researchers can advance the field of computational imaging and sensing, enabling new capabilities and applications in various scientific and industrial domains.