Camera Array Image Splicing via Coordinate Conversion
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Solution Overview
Problem
Acquiring ultra-high-resolution images with a single camera is challenging due to manufacturing limitations and cost constraints, necessitating the use of multiple low-cost cameras in an array, which complicates image splicing processes.
Innovation Solution
A method and apparatus for processing images by converting pixel coordinates from a camera array's image coordinate system to a target plane coordinate system, determining region pixel values for sub-regions, and splicing images based on these values, eliminating the need for feature matching and simplifying the splicing process, especially suitable for large-scale camera arrays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple low-cost cameras are used to form a camera array to acquire ultra-high-resolution images, then the image resolution is improved, but the device complexity and processing time increase
Solution Approach 1:
The patent divides the target image into multiple sub-regions, with each camera in the array responsible for capturing specific sub-images corresponding to these sub-regions. This segmentation approach allows the system to achieve ultra-high-resolution imaging by combining multiple lower-resolution camera inputs, effectively reducing the complexity requirement for individual cameras while maintaining overall high resolution.
Solution Approach 2:
The patent introduces a coordinate system conversion mechanism as an intermediary process. By establishing correspondence relationships between image coordinate systems and target plane coordinate systems, the system enables automated splicing of sub-images without requiring complex feature matching, thus reducing processing complexity while maintaining imaging precision.
2Measurement precision
If traditional feature matching methods are used for splicing images from camera arrays, then image splicing accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent performs coordinate system conversion and establishes correspondence relationships between sub-images and target planes before the actual splicing operation. This preliminary action prepares the data in advance with known coordinate mappings, eliminating the need for time-consuming feature matching during the splicing phase, thus significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent replaces the traditional mechanical feature matching process with a mathematical coordinate system conversion approach. By using internal parameter matrices and external parameter matrices to transform coordinates directly, the system substitutes complex iterative feature recognition and matching operations with efficient linear algebra transformations, reducing both computational complexity and processing time.
3Measurement precision
If coordinate system conversion and region pixel value determination are performed for each pixel, then splicing accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent changes the processing approach from pixel-by-pixel operations to region-based operations. By determining region pixel values for entire sub-regions rather than individual pixels, and by using matrix-based coordinate transformations, the system maintains splicing accuracy while significantly reducing computational complexity through parameter aggregation and mathematical optimization.
Data Source
AI summary
Provided is a method for processing images. The method includes: acquiring a plurality of original sub-images acquired by a plurality of cameras in a camera array; converting first pixel coordinates of each pixel in each original sub-image in an image coordinate system into target pixel coordinates in a target plane coordinate system; determining a region pixel value of each sub-region according to a correspondence between the target pixel coordinates of each pixel in each original sub-image and each sub-region in the target plane; and determining a spliced image in the target plane based on the region pixel value of each sub-region.