Hyperspectral Video Reconstruction via Calibration Matrix Sorting
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Solution Overview
Problem
Current methods for accelerating hyperspectral video reconstruction are hindered by slow preprocessing speeds due to large data volumes and inefficient spectral calibration data traversal, particularly when capturing high-speed dynamic targets and large-area complex scenes, leading to prolonged reconstruction times and reduced accuracy.
Innovation Solution
The method involves acquiring and sorting a calibration matrix for spectral and RGB videos, converting them into a data matrix in a parallel computing manner, and reconstructing hyperspectral video using related calibration points, optimizing data processing and storage to enhance traversal efficiency and reduce invalid computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data optimization methods are used to reduce redundant data, then computation time is reduced, but preprocessing time becomes too long and real-time transfer of preprocessed data is not achieved
Solution Approach 1:
The patent performs spectral calibration data traversal and ordering in advance before the main reconstruction process. The calibration matrix is pre-ordered based on spatial down-sampling constraints, so that during reconstruction, the system only needs to access pre-sorted calibration points rather than performing complex searches, thereby reducing real-time computation time while maintaining preprocessing efficiency
Solution Approach 2:
The patent divides the spectral calibration data into spatially organized segments by ordering the calibration matrix according to spatial down-sampling constraints. This segmentation allows the system to process and traverse calibration data in a structured manner, accessing only relevant calibration points for each spatial region, thus reducing overall computation time without delaying preprocessing
2Productivity
If parallel computing methods are used to accelerate reconstruction, then computation speed is improved, but invalid computations are performed due to linear storage of spectral calibration data
Solution Approach 1:
The patent pre-orders the calibration matrix according to spatial down-sampling constraints before the parallel reconstruction process. This preliminary organization of calibration data ensures that during parallel computation, each thread accesses calibration points in a systematic manner without performing invalid computations, thereby maximizing the efficiency of parallel processing while minimizing wasted computational time
Solution Approach 2:
The patent implements dynamic calibration point retrieval based on spatial down-sampling requirements. Instead of using a static linear storage structure, the system dynamically orders and selects calibration points according to the spatial constraints of each reconstruction region, allowing parallel computing threads to efficiently access only the relevant calibration data for their specific regions, thus reducing invalid computations
3Loss of time
If cropped or sampled spectral video and RGB video are used to decrease spatial pixels, then data amount is reduced and traversal speed is increased, but acceleration problem cannot be completely solved and high-resolution high-accuracy reconstruction is difficult
Solution Approach 1:
The patent performs spectral calibration data ordering and organization in advance based on spatial down-sampling constraints, so that during the main reconstruction process, the system can quickly access pre-organized calibration points without needing to traverse large amounts of data. This preliminary action reduces traversal time while maintaining the full resolution and accuracy of the original data, eliminating the need to crop or sample the input videos
Data Source
AI summary
A method for accelerating hyperspectral video reconstruction includes steps of: acquiring, according to a spectral video and an RGB video captured by a hyperspectral video camera, a calibration matrix of the spectral video and the RGB video; sorting the calibration matrix to generate an ordered calibration matrix; converting, according to the ordered calibration matrix, the spectral video and the RGB video into a data matrix in a parallel manner; acquiring all related calibration points of a reconstruction region according to the ordered calibration matrix; and, reconstructing a hyperspectral video in a parallel manner according to the related calibration points and the data matrix. The related calibration points are acquired by sorting the calibration matrix, such that the number of times the calibration matrix is traverse is reduced, and the computation amount of hyperspectral video reconstruction is decreased.