Computational adaptive optics for high-resolution non-line-of-sight imaging

Non-line-of-sight (NLOS) imaging has aroused great interest during the past few years, by providing a unique solution for the observation of hidden objects behind obstructions or scattering media. As such, NLOS imaging may facilitate broad applications in autonomous driving, remote sensing, and medical diagnosis. However, existing NLOS frameworks suffer from severe degradation of resolution and signal-to-noise ratio (SNR) due to aberrations induced by scattering media and system misalignment, restricting its practical applications. This paper proposes a computational adaptive optics (CAO) method for NLOS imaging to correct optical aberrations in post-processing without the requirement of any hardware modifications. We demonstrate the effectiveness of CAO with a confocal NLOS imaging system in Terahertz (THz) band by imaging different samples behind occlusions for both low- and high-order aberrations. With appropriate metrics used for iterative CAO in post-processing, both the resolution and SNR can be increased by several times without reducing the data acquisition speed.