Noise-adapted Neural Operator for Robust Non-Line-of-Sight Imaging

TL;DR

This paper introduces a noise-adapted neural operator framework for robust 3D non-line-of-sight imaging, combining physical modeling, deep algorithm unfolding, and feature fusion to improve accuracy and robustness in challenging conditions.

Contribution

It develops a novel operator learning-based inverse problem framework with adaptive noise assessment and feature fusion, advancing NLOS imaging reconstruction techniques.

Findings

Achieves fast and accurate 3D reconstruction on simulated and real data.

Demonstrates robustness to noise and sparse data conditions.

Enhances image quality by integrating global and local spatiotemporal features.

Abstract

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Computational imaging, especially non-line-of-sight (NLOS) imaging, the extraction of information from obscured or hidden scenes is achieved through the utilization of indirect light signals resulting from multiple reflections or scattering. The inherently weak nature of these signals, coupled with their susceptibility to noise, necessitates the integration of physical processes to ensure accurate reconstruction. This paper presents a parameterized inverse problem framework tailored for large-scale linear problems in 3D imaging reconstruction. Initially, a noise estimation module is employed to adaptively assess the noise levels present in transient data. Subsequently, a parameterized neural operator is developed to approximate the inverse mapping, facilitating end-to-end rapid image reconstruction. Our 3D image reconstruction framework, grounded in operator learning, is constructed through deep algorithm unfolding, which not only provides commendable model interpretability but also enables dynamic adaptation to varying noise levels in the acquired data, thereby ensuring consistently robust and accurate reconstruction outcomes. Furthermore, we introduce a novel method for the fusion of global and local spatiotemporal data features. By integrating structural and detailed information, this method significantly enhances both accuracy and robustness. Comprehensive numerical experiments conducted on both simulated and real datasets substantiate the efficacy of the proposed method. It demonstrates remarkable performance with fast scanning data and sparse illumination point data, offering a viable solution for NLOS imaging in complex scenarios.