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

arXiv: https://arxiv.org/abs/2510.04883 · HTML: https://mlsanity.com/paper/2510.04883/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method](https://mlsanity.com/paper/2212.11548/) (2022)
- [Low-light Image Enhancement via Breaking Down the Darkness](https://mlsanity.com/paper/2111.15557/) (2021)
- [HVI: A New Color Space for Low-light Image Enhancement](https://mlsanity.com/paper/2502.20272/) (2025)
- [A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement](https://mlsanity.com/paper/1711.00591/) (2017)
- [Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representations](https://mlsanity.com/paper/2407.12511/) (2024)
- [Burst Photography for Learning to Enhance Extremely Dark Images](https://mlsanity.com/paper/2006.09845/) (2020)
- [Fixed Pattern Noise Reduction for Infrared Images Based on Cascade Residual Attention CNN](https://mlsanity.com/paper/1910.09858/) (2019)
- [Semi-supervised atmospheric component learning in low-light image problem](https://mlsanity.com/paper/2204.07546/) (2022)
- [Unsupervised network for low-light enhancement](https://mlsanity.com/paper/2306.02883/) (2023)
- [Active Illumination for Visual Ego-Motion Estimation in the Dark](https://mlsanity.com/paper/2502.13708/) (2025)

## Abstract (arXiv)

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.