用单一频率提升iToF成像精度与多目标分离能力
Multipath Interference Suppression in Indirect Time-of-Flight Imaging via a Novel Compressed Sensing Framework
- 基于多相位偏移和窄占空比波形构建感知矩阵
- 在真实调制响应下实现更准的深度重建与抗干扰能力
- 适合希望不改硬件却提升成像质量的研究者
我们提出一种新型压缩感知方法，以提升间接飞行时间（iToF）系统的深度重建精度与多目标分离能力。与依赖硬件改进、复杂调制或繁复数据驱动重建的传统方法不同，本方法仅使用单一调制频率，通过多相位偏移和窄占空比连续波构建感知矩阵，并考虑由镜头畸变引起的像素级距离变化，使矩阵更贴合实际调制响应特性。为增强稀疏恢复性能，采用K-Means聚类对距离响应字典进行分组，在正交匹配追踪（OMP）过程中限制原子选择范围，有效缩小搜索空间并提高解的稳定性。实验表明，该方法在重建精度和鲁棒性方面均优于传统方法，且无需任何额外硬件改动。
原文摘要 · Abstract (English)
We propose a novel compressed sensing method to improve the depth reconstruction accuracy and multi-target separation capability of indirect Time-of-Flight (iToF) systems. Unlike traditional approaches that rely on hardware modifications, complex modulation, or cumbersome data-driven reconstruction, our method operates with a single modulation frequency and constructs the sensing matrix using multiple phase shifts and narrow-duty-cycle continuous waves. During matrix construction, we further account for pixel-wise range variation caused by lens distortion, making the sensing matrix better aligned with actual modulation response characteristics. To enhance sparse recovery, we apply K-Means clustering to the distance response dictionary and constrain atom selection within each cluster during the OMP process, which effectively reduces the search space and improves solution stability. Experimental results demonstrate that the proposed method outperforms traditional approaches in both reconstruction accuracy and robustness, without requiring any additional hardware changes.
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