Learning to Enhance Aperture Phasor Field for Non-Line-of-Sight Imaging

TL;DR

This paper introduces a phasor-based enhancement network for non-line-of-sight imaging that reduces sampling requirements and improves measurement reconstruction by focusing on informative frequency ranges.

Contribution

It proposes a novel phasor-based pipeline and denoising autoencoder approach to enhance NLOS imaging with fewer samples and smaller apertures.

Findings

Achieves 16x to 64x fewer samplings in practical scenarios.

Enables imaging with 4x smaller apertures.

Improves measurement reconstruction quality.

Abstract

This paper aims to facilitate more practical NLOS imaging by reducing the number of samplings and scan areas. To this end, we introduce a phasor-based enhancement network that is capable of predicting clean and full measurements from noisy partial observations. We leverage a denoising autoencoder scheme to acquire rich and noise-robust representations in the measurement space. Through this pipeline, our enhancement network is trained to accurately reconstruct complete measurements from their corrupted and partial counterparts. However, we observe that the \naive application of denoising often yields degraded and over-smoothed results, caused by unnecessary and spurious frequency signals present in measurements. To address this issue, we introduce a phasor-based pipeline designed to limit the spectrum of our network to the frequency range of interests, where the majority of informative signals are detected. The phasor wavefronts at the aperture, which are band-limited signals, are employed as inputs and outputs of the network, guiding our network to learn from the frequency range of interests and discard unnecessary information. The experimental results in more practical acquisition scenarios demonstrate that we can look around the corners with $16\times$ or $64\times$ fewer samplings and $4\times$ smaller apertures. Our code is available at https://github.com/join16/LEAP.