Improving non-line-of-sight image reconstruction with weighting factors
Xiaohua Feng, Liang Gao
Abstract
Non-line-of-sight (NLOS) imaging is a light-starving application that suffers from highly noisy measurement data. In order to recover the hidden scene with good contrast, it is crucial for the reconstruction algorithm to be robust against noises and artifacts. We propose here two weighting factors for the filtered backprojection (FBP) reconstruction algorithm in NLOS imaging. The apodization factor modifies the aperture (wall) function to reduce streaking artifacts, and the coherence factor evaluates the spatial coherence of measured signals for noise suppression. Both factors are simple to evaluate, and their synergistic effects lead to state-of-the-art reconstruction quality for FBP with noisy data. We demonstrate the effectiveness of the proposed weighting factors on publicly accessible experimental datasets.