Neural Network Phase Error Prediction for Optical Imaging Latency
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Solution Overview
Problem
Existing digital image processing systems face challenges in reducing latency for phase error estimation in optical imaging, particularly in real-time applications, due to computational delays and iterative methods that result in stale phase estimates, degrading image quality and beam correction accuracy.
Innovation Solution
The implementation of a machine learning-based Adaptive Temporal Phase Prediction (ATPP) system using neural networks to predict future phase errors from past sequences, reducing latency by providing improved initial conditions and compensating for computational delays in digital holographic systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative methods (DH-MBIR) are used for phase error estimation, then measurement precision is improved, but loss of time increases due to computational latency
Solution Approach 1:
The system performs preliminary action by using the current phase error estimate to predict and initialize the next phase error estimate before the iterative computation is complete. This predictive initialization reduces the computational iterations needed while maintaining accuracy, directly addressing the latency-precision tradeoff.
Solution Approach 2:
The system implements feedback by continuously using the most recent phase error estimate to inform and initialize subsequent estimates. This closed-loop feedback mechanism ensures that each computational cycle builds on the latest information, reducing redundant computations and latency while preserving measurement precision.
2Productivity
If more processors are used for parallel computation, then productivity increases, but device complexity increases
Solution Approach 1:
The system applies self-service by having each computational process use its own previous output as input for the next computation. This self-referential approach eliminates the need for complex inter-processor communication and data sharing mechanisms, achieving high throughput with simpler device architecture.
3Measurement precision
If iterative computation is performed, then measurement precision is improved, but loss of time increases due to multiple iterations
Solution Approach 1:
The system performs preliminary action by pre-initializing the iterative computation with a predicted phase error estimate derived from the current estimate. This preliminary initialization gets the iterative process closer to the solution from the start, reducing the number of iterations required to achieve the desired measurement precision.
Data Source
AI summary
A computer-implemented method of latency reduction is disclosed for a digital holography optical imaging system. The method comprises receiving an incoming light field at a focal plane array of the digital holography optical imaging system, applying an interfering light field to the incoming light field at the focal plane array, and generating a holographic image based on the incoming light field and the interfering light field at the focal plane array. The method further comprises generating a sequence of phase errors based on the holographic image, generating at least one training parameter based on the sequence of phase errors, and training a neural network to revise the at least one training parameter using the sequence of phase errors, a time delay, and the at least one training parameter. The method further comprises predicting a future phase error for a future holographic image based on the revised training parameter.