Non-Line-of-Sight Long-Wave Infrared Imaging based on Nestformer

Due to its portability, low cost, and unnoticed detection mode, passive non-line-of-sight (NLOS) imaging has garnered widespread attention in recent years. This technology reconstructs hidden objects beyond the direct line of sight by analyzing the diffuse reflection on a relay surface. However, issues such as scene complexity and interference from ambient light lead to suboptimal reconstruction results. Long-wave infrared (LWIR) imaging is more significantly affected by ambient temperature but less by illumination, resulting superior resistance to environmental light interference. Therefore, we conduct NLOS imaging experiments using an LWIR camera and propose a novel NLOS image reconstruction network called Nestformer. The feature extraction component of this network incorporates a Transformer-CNN dual-branch mechanism. This dual-branch mechanism includes a Base Transformer Module for capturing global features and a Spatial Channel Attention Module that focuses on extracting local information. Additionally, to enhance the robustness of our model, a combination of multiple loss functions is employed to optimize its feedback capability. Experimental results on our self-collected NLOS-LR dataset indicate that Nestformer outperforms current passive NLOS imaging methods in terms of reconstruction performance.