Light Field Imager Sub-Aperture Selection for Barcode Scanning
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Solution Overview
Problem
Light field imaging systems for barcode scanning face high computational complexity in refocusing operations, leading to unacceptably long delays between image capture and symbol decoding due to the need to analyze and combine multiple sub-aperture images, which is exacerbated by the lack of robustness to mechanical shocks and limited depth of field in traditional scanners.
Innovation Solution
A light field imager with a microlens array and sensor that estimates signal-to-noise ratio and depth, selects an optimal subset of sub-aperture images based on these parameters, and combines them using quantized shifting and adding, reducing computational complexity by using a minimal number of images necessary for image analysis while meeting a minimum signal-to-noise ratio threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If light field imaging systems are used to increase depth of field and robustness, then imaging robustness and depth of field are improved, but computational complexity increases leading to longer processing delays
Solution Approach 1:
The patent extracts only the necessary subset of sub-aperture images required for successful barcode decoding rather than processing all captured images. By selecting a minimal subset that meets the signal-to-noise ratio threshold, the system achieves fast refocusing with reduced computational complexity while maintaining imaging robustness.
Solution Approach 2:
The patent applies partial action by processing only a portion of the available sub-aperture images rather than all of them. The system determines the minimum number of images needed to achieve sufficient signal-to-noise ratio for decoding, thereby reducing processing time while maintaining decoding accuracy.
2Measurement precision
If all sub-aperture images are combined to ensure sufficient signal-to-noise ratio, then signal quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary evaluation of sub-aperture images to identify and select only those that contribute meaningfully to the final signal-to-noise ratio. By pre-assessing image quality and selecting an optimal subset before combining, the system avoids the computational burden of processing all images while ensuring sufficient signal quality for decoding.
Solution Approach 2:
The patent changes the parameter of image selection from processing all sub-aperture images to processing a selected subset based on quality metrics. By adjusting the selection criteria and subset size dynamically, the system optimizes the balance between signal-to-noise ratio and computational complexity.
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 significantly reduces computational complexity, achieving fast refocusing and image analysis with up to a 99% reduction in processing time compared to conventional methods, enabling robust and efficient barcode scanning across a larger depth of field.
Implementation Method 1
Light field cameras use a microlens array in the optical path to capture a set of light rays that can be combined in software to produce images focused at various distances
Data Source
Figure 1A~1B
Figure 2A~2B
Figure 2C~2D
AI summary
An imaging device has a light field imager and a processor. The light field imager includes a microlens array, and a light field sensor positioned proximate to the microlens array, having a plurality of pixels and recording light field data of an optical target from light passing through the microlens array. The processor is configured to: receive the light field data of the optical target from the light field sensor, estimate signal to noise ratio and depth of the optical target in the light field data, select a subset of sub-aperture images based on the signal to noise ratio and depth, combine the selected subset of sub-aperture images, and perform image analysis on the combined subset of sub-aperture images.