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

TL;DR

This paper introduces a compressed sensing approach for indirect ToF imaging that enhances depth accuracy and multi-target separation using a single modulation frequency, without hardware modifications.

Contribution

It presents a novel compressed sensing framework that accounts for lens distortion and employs clustering to improve sparse recovery in iToF systems.

Findings

Outperforms traditional methods in depth reconstruction accuracy

Enhances robustness against noise and distortions

Operates with minimal hardware modifications

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.