Electrically Tunable Lens Phase Unwrapping for Fast MSL 3D Imaging
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Solution Overview
Problem
Microscopic structured-light (MSL) 3D imaging systems face limitations in shallow depth of field (DOF) and require multiple fringe patterns for 3D point cloud reconstruction, leading to slower imaging speeds and challenges in phase unwrapping, especially with existing phase unwrapping algorithms requiring additional images or large training datasets.
Innovation Solution
Utilizing an electrically tunable lens (ETL) to adjust focal settings and implement a geometric-constraint phase unwrapping (GCPU) algorithm, which estimates focal plane positions to unwrap phase maps pixel-by-pixel, reducing the need for additional patterns and enhancing imaging speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple fringe patterns are used for 3D point cloud reconstruction, then measurement precision is improved, but imaging speed deteriorates
Solution Approach 1:
The patent applies preliminary action by using focus metric analysis on captured images to pre-determine which images contain in-focus pixels before phase unwrapping. This preliminary classification allows the system to selectively process only necessary images, reducing the total number of fringe patterns needed while maintaining 3D reconstruction accuracy.
Solution Approach 2:
The patent changes the parameter of image selection criteria by introducing focus metric thresholds. Instead of using all captured fringe patterns, the system adjusts the focus metric parameter to identify and select only those images where pixels are in focus, thereby reducing the number of patterns required for accurate 3D reconstruction.
2Measurement precision
If traditional phase unwrapping algorithms are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by treating different pixels differently based on their focus status. Instead of applying uniform phase unwrapping to all pixels across all images, the system identifies in-focus pixels through focus metric analysis and applies phase unwrapping only to those specific pixels, simplifying the overall algorithm while maintaining accuracy.
Solution Approach 2:
The patent segments the phase unwrapping process by separating in-focus pixels from out-of-focus pixels using focus metric thresholds. This segmentation allows the system to process only the relevant in-focus pixels, reducing computational complexity while preserving measurement precision.
3Reliability
If additional fringe patterns are captured for phase unwrapping, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary focus metric analysis on captured images to determine which images contain in-focus pixels before proceeding with phase unwrapping. This preliminary action ensures that reliable phase information is obtained from the correct images without requiring additional fringe patterns, thus maintaining reliability while reducing imaging time.
Solution Approach 2:
The system uses the focus metric information inherently present in the captured images to self-determine which images are suitable for phase unwrapping. This self-service approach eliminates the need for additional reference patterns or external validation, maintaining reliability while minimizing time loss.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Achieves a larger depth of field with high spatial resolution and efficient 3D reconstruction by minimizing the number of required fringe patterns, improving imaging speed and accuracy in MSL systems.
Implementation Method 1
an electrically tunable lens (ETL) to adjust focal settings
Data Source
AI summary
Described herein are systems and methods for generating three-dimension point clouds. Phase-shifted images of a sample are captured using a camera. Fring contrast maps are generated based on the phase-shifted images. A label map is generated based on the fringe contrast maps. In-focus pixels are extracted from the phase-shifted images to generate a wrapped in-focus phase map. A rough depth map is generated based on the label map. An artificial phase map is generated based on the rough depth map. The wrapped in-focus phase map is unwrapped and a three-dimensional point cloud is generated based on the unwrapped in-focus phase map.