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Accelerating digital volume correlation computation using optimized sub-volume downsampling
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AI
DOI：10.1016/j.optlaseng.2025.109247.png)
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Abstract
En 中文
• A novel optimized subvolume downsampling strategy is proposed to accelerate DVC computation without compromising subvoxel registration precision. • Voxel significance is evaluated using a mean intensity gradient (MIGmean) indicator. • An adaptive threshold determination approach ensures robust voxel selection based on local features. • The proposed strategy reduces per-point computation time by up to 82.80 % in simulation and 69.18 % in real tests. • The strategy is compatible with advanced self-adaptive DVC algorithms.
Keywords:
subvolume downsampling
DVC computation
mean intensity gradient
adaptive threshold
subvoxel registration
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