  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.
