Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media (1806.04139v2)

Abstract: Imaging through scattering is an important, yet challenging problem. Tremendous progress has been made by exploiting the deterministic input-output "transmission matrix" for a fixed medium. However, this "one-to-one" mapping is highly susceptible to speckle decorrelations - small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical "one-to-all" deep learning technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.

• The paper introduces a CNN-based model that learns statistical invariance from speckle patterns to achieve scalable imaging through diverse scattering media.
• It employs an enhanced U-Net architecture with dense blocks to overcome traditional transmission matrix limitations and mitigate speckle decorrelation.
• Empirical results validated by metrics like PCC and JI demonstrate improved generalization and accuracy across unseen diffuser configurations.

Deep Speckle Correlation: A Deep Learning Approach Towards Scalable Imaging through Scattering Media

The paper "Deep Speckle Correlation: a Deep Learning Approach Towards Scalable Imaging through Scattering Media" presents a sophisticated deep learning framework to address the ongoing challenge of imaging through complex scattering media. Traditional imaging techniques in scattering media often rely on the deterministic ‘one-to-one’ transmission matrix (TM) framework, which attempts to map the input-output relation for a fixed medium. While significant progress has been made, these methods are highly susceptible to perturbations and speckle decorrelations, significantly limiting their scalability and robustness.

Key Contributions

The authors propose a novel ‘one-to-all’ statistical deep learning model, building on a convolutional neural network (CNN) to effectively learn and generalize from the statistical variations inherent in speckle intensity patterns across multiple diffusers of the same macroscopic class. This approach marks a departure from existing techniques by embracing variability and focusing on statistical invariance rather than a fixed deterministic input-output model.

1. CNN Architecture and Training: The CNN is designed in an encoder-decoder format akin to the U-Net architecture, but with enhancements to handle the high complexity and non-linearity of the scattering process. The network incorporates dense blocks to improve training efficiency and is trained to predict object images utilizing speckle data acquired from a range of diffusers.
2. Scalability Enhancement: The authors effectively demonstrate the model's capacity to generalize and make accurate image predictions across previously unseen diffuser configurations. This is verified by showcasing robustness against speckle decorrelations, facilitated by training across multiple scatterers of the same class, enhancing both statistical and measurement scalability.
3. Comparison with Traditional Methods: Unlike phase-sensitive TM approaches hindered by decorrelation, the proposed CNN model provides a viable alternative, leveraging its ability to learn from data representing multiple scattering configurations to predict through unseen media with improved accuracy and resistance to decorrelation.

Results and Evaluation

The paper provides strong empirical evidence supporting the proposed method's efficacy. The trained model successfully predicts objects through unseen diffusers, demonstrating its generalization capabilities. Quantitative metrics, such as the Pearson Correlation Coefficient (PCC) and the Jaccard Index (JI), are employed to evaluate the model's performance, reaffirming its superiority over traditional single-diffuser trained models, which fail as speckle patterns become decorrelated.

Theoretical and Practical Implications

This work significantly advances the field of computational imaging through scattering media, proposing a framework that mitigates the limitations imposed by dynamic scatterer environments. It aligns well with broader trends in deep learning by utilizing data-driven approaches to solve complex problems where parametric models are infeasible or insufficient.

The theoretical insights gleaned from the network's ability to isolate invariant statistical features may inform future models and applications, potentially extending beyond current biological imaging challenges to other domains requiring robust imaging capabilities through dynamic media.

Future Directions

Further exploration into the application of this deep learning framework in biological systems could leverage macroscopic scattering parameters to enhance imaging fidelity in complex tissue environments. Additionally, expanding this framework to incorporate volumetric scattering conditions remains a critical next step, aiming for real-time adaptive optical systems that can dynamically adjust to changing conditions without computational or hardware overheads.

This paper provides a foundational approach that promises to facilitate more accurate and scalable imaging methodologies in challenging scattering environments, opening avenues for broader applications in biomedical imaging, security, and beyond.