Multipath Interference Suppression in Indirect Time-of-Flight Imaging via a Novel Compressed Sensing Framework

2025-07-23

AI Summary

To address depth reconstruction distortion and degraded multi-target resolution caused by multipath interference in indirect time-of-flight (iToF) imaging, this paper proposes a hardware-free compressive sensing framework. Methodologically, it employs a single-frequency continuous wave with narrow duty cycle combined with multi-phase shift modulation to construct a sensing matrix better aligned with the practical modulation response, and incorporates a pixel-wise lens distortion compensation model. Furthermore, it integrates a K-means clustering–driven atom selection constraint into orthogonal matching pursuit (OMP) for sparse signal recovery, significantly reducing the search space and enhancing solution stability. Experimental results demonstrate that the proposed method outperforms conventional iToF algorithms in both depth accuracy and multipath robustness, while effectively improving multi-target separation capability.

Technology Category

• Computer Vision: Multi-modal Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Suppress multipath interference in iToF imaging
• Enhance depth accuracy with compressed sensing
• Improve multi-target separation using single modulation

Innovation

• Compressed sensing with single modulation frequency
• Pixel-wise range variation in sensing matrix
• K-Means clustering for sparse recovery enhancement