LiDAR Super-Resolution via Sub-Pixel Image Matching
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Solution Overview
Problem
Scanning type LiDAR systems face challenges in acquiring dense detection data due to large gaps between sensing points, particularly in directions perpendicular to the scanning direction, resulting in low-density and low-resolution detection data.
Innovation Solution
An image processing device and method that acquire reflected light images and background light images, perform sub-pixel matching on these images to generate alignment information, and then use this information to perform a super-resolution process, effectively increasing the resolution of the reflected light images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scanning light is used to measure distance, then distance measurement capability is achieved, but large gaps between sensing points occur resulting in low detection data density
Solution Approach 1:
The patent combines multiple low-resolution reflected light images into a single high-resolution image through super-resolution processing. By merging information from multiple images captured at different scanning positions, the system achieves both accurate distance measurement and high detection data density without requiring additional sensors or changing the fundamental scanning mechanism.
Solution Approach 2:
The patent transitions from one-dimensional scanning data to two-dimensional image data by capturing reflected light intensity distributions across multiple scanning lines. This dimensional transformation allows the system to fill gaps between sensing points by utilizing spatial information from adjacent scanning lines, thereby increasing detection data density while maintaining measurement precision.
2Measurement precision
If scanning light is used to measure distance, then distance information is obtained, but image resolution remains low due to sparse sensing points
Solution Approach 1:
The patent performs preliminary alignment processing by calculating displacement amounts between scanning lines before conducting super-resolution processing. This preliminary action of pre-aligning multiple images based on detected displacement ensures that the subsequent super-resolution processing can effectively combine information from multiple sources, thereby achieving high image resolution while preserving the accurate distance information obtained from the scanning measurements.
Solution Approach 2:
The patent replaces the need for mechanically increasing sensor density with a computational approach. Instead of adding more physical sensors to reduce gaps between sensing points, the system uses super-resolution processing algorithms to synthesize high-resolution images from multiple low-resolution images, thereby achieving high image resolution without modifying the mechanical scanning system.
3Manufacturing precision
If multiple images are processed to increase resolution, then image resolution improves, but processing complexity increases
Solution Approach 1:
The patent employs self-service mechanisms where the processing system automatically detects displacement amounts between scanning lines and performs alignment without requiring manual intervention or complex external control systems. The super-resolution processing unit autonomously combines multiple images using the detected displacement information, thereby reducing processing complexity while achieving high image resolution.
Solution Approach 2:
The patent changes the processing parameters by using detected displacement amounts to guide the super-resolution processing. Instead of using fixed or complex adaptive parameters, the system utilizes the measured displacement information to control the alignment and combination process, thereby simplifying the processing complexity while maintaining high image resolution output.
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 proposed solution enables the generation of high-resolution images from LiDAR data, improving the density and quality of detection data, which is essential for accurate object recognition and automated driving systems.
Implementation Method 1
the reflected light image containing distance information obtained by emitting light and detecting the light reflected from an object by a light receiving element
Implementation Method 2
the background light image containing luminance information obtained by detecting background light relative to the light reflected from the object by the light receiving element
Data Source
AI summary
An image processing device includes an image acquiring unit, a matching processing unit, and a super-resolution processing unit. The image acquiring unit is configured to acquire a reflected light image and a background light image in association with each other. The matching processing unit is configured to a plurality of first images, each of which is one of the reflected light image and the background light image, and perform a sub-pixel matching process on the plurality of first images. The super-resolution processing unit is configured to perform a super-resolution process on a plurality of second images, each of which is the other one of the reflected light image and the background light image, using alignment information acquired by the sub-pixel matching process to generate a high-resolution image.