用深度网络从有干扰的红外图像中重建清晰画面，让机器人在暗光下也能精准感知。
CLEAR-IR: Clarity-Enhanced Active Reconstruction of Infrared Imagery
- 基于多尺度感知的深度架构，有效去除主动红外发射图案干扰
- 重建后的图像质量显著提升，下游任务性能超越现有方法
- 可模拟可见光风格，使原训练于RGB的机器人任务在无光环境下运行
本文提出一种新方法，利用红外流实现黑暗环境下的鲁棒机器人感知。红外图像在低光条件下比可见光更抗噪，但受主动发射模式主导，影响目标检测、跟踪与定位等高层任务。为此，提出受DeepMAO启发的深度多尺度感知过完备架构，从含发射图案的输入中重建干净红外图像，同时提升图像质量与下游机器人性能。该方法优于现有增强技术，使视觉驱动的机器人系统可在从明亮到极端低光的各种光照条件下稳定运行。结果表明，该方法能模拟场景的可见光风格，并适用于原本在RGB数据上训练的机器人任务，实现无需机载照明的极暗光环境作业。
原文摘要 · Abstract (English)
This paper presents a novel approach for enabling robust robotic perception in dark environments using infrared (IR) stream. IR stream is less susceptible to noise than RGB in low-light conditions. However, it is dominated by active emitter patterns that hinder high-level tasks such as object detection, tracking and localisation. To address this, a Deep Multi-scale Aware Overcomplete (DeepMAO) inspired architecture is proposed that reconstructs clean IR images from emitter populated input, improving both image quality and downstream robotic performance. This approach outperforms existing enhancement techniques and enables reliable operation of vision driven robotic systems across illumination conditions from well-lit to extreme low-light scenes. The results outline the ability of this work to be able to mimic RGB styling from the scene and its applicability on robotics tasks that were trained on RGB images, opening the possibility of doing these tasks in extreme low-light without on-board lighting.
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