用三维奇异值分解压缩生物体数据，重建快且精度高。
Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images
- 将三维生物图像用结构化3D-SVD表示，支持渐进式重建。
- 在鱼和脑部数据上，重建质量接近塔克分解，计算更快。
- 低截断率即可保留主要结构，适合医学影像压缩与分析。
本文提出一种实用的框架Structured 3D-SVD，用于生物体数据的重建、压缩与分析。受矩阵奇异值分解（SVD）逻辑启发，该方法在空间域中表示三阶体数据，并通过有序的拟奇异系数实现渐进式重建。实验在两个生物体数据集上进行：一条鱼的全体积扫描和一个脑部扫描。结果表明，Structured 3D-SVD的重建质量接近塔克分解（Tucker decomposition），但计算时间更短，且在准确性和运行时间上均优于经典多线性分解（CPD）。此外，渐进重建分析显示，较低的截断级别即可保留主要体结构，更高截断级别则带来更精细的重建。
原文摘要 · Abstract (English)
This work introduces Structured 3D-SVD as a practical framework for the reconstruction, compression, and analysis of biological volumetric data. Inspired by the logic of matrix singular value decomposition (SVD), the proposed approach represents third-order volumetric data in the spatial domain and supports progressive reconstruction through ordered quasi-singular coeffients. The experimental evaluation was carried out on two biological volumetric datasets: one full-volume scan of a fish and another of a brain. The results show that Structured 3D-SVD achieves reconstruction quality close to that of Tucker decomposition while requiring shorter computation times and outperforms canonical polyadic decomposition (CPD) in both accuracy and runtime. In addition, a progressive reconstruction analysis shows that relatively low truncation levels are sufficient to preserve the main volumetric structures, while higher truncation levels lead to more detailed reconstructions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。