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

• URL: http://arxiv.org/abs/2510.04883v1
• Date: Mon, 06 Oct 2025 15:04:56 GMT
• Title: CLEAR-IR: Clarity-Enhanced Active Reconstruction of Infrared Imagery
• Authors: Nathan Shankar, Pawel Ladosz, Hujun Yin,
• Abstract summary: This paper presents a novel approach for enabling robust robotic perception in dark environments using infrared (IR) stream.<n>It is dominated by active emitter patterns that hinder high-level tasks such as object detection, tracking and localisation.<n>A U-Net-based architecture is proposed that reconstructs clean IR images from emitter-populated input, improving both image quality and downstream robotic performance.
• Score: 3.490087692799367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. To 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.

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