Correlation Imaging Patterning for High-Frame-Rate Vehicle Lamps
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Solution Overview
Problem
Quantum radar systems require multiple irradiations to reconstruct an image, resulting in a low frame rate, making them less effective in good-visibility environments compared to traditional two-dimensional image sensors.
Innovation Solution
An imaging apparatus using correlation calculation with a light source generating a uniform intensity distribution, a patterning device modulating the light beam based on two-gradation first image data, and a processing device reconstructing the image using m-gradation second image data, allowing for high image quality and high frame rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum radar uses reference light multiple times to reconstruct an image, then image quality is improved, but frame rate deteriorates
Solution Approach 1:
The invention segments the high-resolution imaging function from the frame rate requirement by using a single-pixel detector that sequentially measures different spatial regions. The large aperture is divided into multiple measurement zones that are scanned over time, allowing each zone to be measured with high precision while maintaining overall system productivity through efficient measurement sequencing.
Solution Approach 2:
The invention transitions from spatial resolution to temporal resolution by measuring different spatial positions at different time points. The single-pixel detector measures light intensity from different spatial regions sequentially, and the processing device reconstructs the two-dimensional image by correlating these temporal measurements with the known spatial configuration, effectively trading spatial simultaneity for temporal sequencing.
2Measurement precision
If quantum radar is used in good-visibility environment, then image quality can be achieved, but frame rate remains low compared to two-dimensional image sensor
Solution Approach 1:
The invention changes the measurement parameters by adjusting the aperture size and measurement time allocation. The large aperture is divided into multiple zones with different measurement durations, allowing optimal allocation of measurement resources. High-priority regions can be measured with longer integration times for higher quality, while other regions use shorter measurement times, overall reducing the total time consumption compared to uniform high-resolution imaging.
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
The solution provides both high image quality and high frame rates, combining the advantages of quantum radar and two-dimensional image sensors, enabling effective image capture in various visibility conditions.
Implementation Method 1
a light source structured to generate a light beam having a uniform intensity distribution
Implementation Method 2
a patterning device structured to modulate the intensity distribution of the light beam according to two-gradation (two-level) first image data
Implementation Method 3
a photodetector structured to receive reflected light from an object
Data Source
Figure 1
Figure 2A~2C
Figure 3
AI summary
Patterning device 114 modulates a light beam intensity distribution according to two-gradation first image data IMG1 having a first number of pixels. Processing device 130 calculates a correlation between detection intensity b based on output of photodetector 120 and m-gradation (m ≥ 2) random second image data IMG2 having a second number of pixels smaller than the first number, to generate a reconstructed image G(x,y) of an object. When the first and second image data IMG1 and IMG2 are scaled to the same size and overlapped, each pixel is associated with a pixel group including multiple pixels of the overlapping first image data IMG1. With the normalized gradation value of a given pixel as k, the number of pixels included in the pixel group as L, and the number of pixels having a value of 1 in the pixel group as 1, 1 = L x k holds true.