CLEAR-IR: Clarity-Enhanced Active Reconstruction of Infrared Imagery

Abstract--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. T o address this, a U-Net-based 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. Lighting-invariant vision systems are desirable for enabling robots to operate robustly across diverse and unpredictable environments without requiring modifications to the underlying perception pipeline. In order to support high-level tasks such as object detection, semantic segmentation, and image classification, the vision system must remain reliable even in low light or completely dark scenes. Such capabilities are critical in domains like mine shaft exploration, post-disaster victim identification, nuclear facility inspection, and visual loop closure in feature-deprived environments using aruco markers.