3D Integral Imaging for Low Illumination Object Recognition
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Solution Overview
Problem
Conventional image sensors struggle with capturing and recognizing 3D objects in low illumination conditions due to dominant read-noise and poor signal-to-noise ratio, making it difficult to visualize and recognize scenes, and existing low-light imaging solutions like EM-CCD and sCMOS cameras are expensive and bulky.
Innovation Solution
The use of 3D integral imaging with low-cost passive image sensors operating in the visible spectrum, which captures elemental images and reconstructs the 3D scene using a virtual pinhole array, followed by total-variation regularization for de-noising and object recognition using a convolutional neural network pre-trained for low-light conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional image sensors are used in low illumination conditions, then device cost and complexity are reduced, but signal-to-noise ratio deteriorates and image quality becomes poor
Solution Approach 1:
The patent transitions from 2D image capture to 3D integral imaging by capturing light field information across multiple angular perspectives. This dimensional expansion allows the system to separate signal from noise through spatial-frequency domain processing, achieving superior signal-to-noise ratio in low illumination conditions while using conventional sensors.
Solution Approach 2:
The patent segments the light field into multiple elemental images captured from different angular positions. By dividing the imaging task across multiple spatial perspectives and processing them through 3D reconstruction algorithms, the system enhances signal-to-noise ratio while maintaining device simplicity.
2Measurement precision
If EM-CCD or sCMOS cameras are used for low-light imaging, then signal-to-noise ratio is improved, but device cost and bulk increase
Solution Approach 1:
The patent creates multiple virtual copies of the imaging process by capturing elemental images from different angular positions using a single conventional camera. These copies are then processed through 3D reconstruction to achieve enhanced signal-to-noise ratio, eliminating the need for expensive EM-CCD or sCMOS cameras.
Solution Approach 2:
The patent makes a conventional 2D camera perform multiple functions by capturing both spatial and angular information simultaneously through integral imaging. This multi-functionality allows the simple device to achieve low-light performance previously requiring complex specialized cameras.
3Difficulty of detecting and measuring
If active infrared sources are used to illuminate the scene, then object detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent converts the harmful effect of read-noise in low illumination conditions into a benefit by using 3D integral imaging reconstruction. The reconstruction process inherently filters noise while preserving signal, turning the read-noise dominant situation into an opportunity for enhanced object detection without active illumination.
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
This approach enhances the signal-to-noise ratio, improves image visualization, and enables effective object detection and classification in low-light environments, including facial recognition, without the need for active infrared sources or expensive optics.
Implementation Method 1
reconstruct the 3D scene by back-propagating captured light rays from the plurality of elemental images through the virtual pinhole array to a specified depth plane
Data Source
AI summary
Described herein is an object recognition system in low illumination conditions. A 3D InIm system can be trained in the low illumination levels to classify 3D objects obtained under low illumination conditions. Regions of interest obtained from 3D reconstructed images are obtained by de-noising the 3D reconstructed image using total-variation regularization using an augmented Lagrange approach followed by face detection. The regions of interest are then inputted into a trained CNN. The CNN can be trained using 3D InIm reconstructed under low illumination after TV-denoising. The elemental images were obtained under various low illumination conditions having different SNRs. The CNN can effectively recognize the 3D reconstructed faces after TV-denoising.