# Infrared Colorization Using Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1604.02245 · HTML: https://mlsanity.com/paper/1604.02245/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2016.

## Built on by (most cited first, 7 of 7)

- [Robust pedestrian detection in thermal imagery using synthesized images](https://mlsanity.com/paper/2102.02005/) (2021)
- [Visibility Constrained Wide-band Illumination Spectrum Design for Seeing-in-the-Dark](https://mlsanity.com/paper/2303.11642/) (2023)
- [ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime Infrared to Daytime Visible Video Translation](https://mlsanity.com/paper/2204.12367/) (2022)
- [Attention-Guided NIR Image Colorization via Adaptive Fusion of Semantic and Texture Clues](https://mlsanity.com/paper/2107.09237/) (2021)
- [MAC: ModAlity Calibration for Object Detection](https://mlsanity.com/paper/2310.09461/) (2023)
- [MTSIC: Multi-stage Transformer-based GAN for Spectral Infrared Image Colorization](https://mlsanity.com/paper/2506.17540/) (2025)
- [Weakly-Supervised Semantic Segmentation of Ships Using Thermal Imagery](https://mlsanity.com/paper/2212.13170/) (2022)

## Abstract (arXiv)

This paper proposes a method for transferring the RGB color spectrum to near-infrared (NIR) images using deep multi-scale convolutional neural networks. A direct and integrated transfer between NIR and RGB pixels is trained. The trained model does not require any user guidance or a reference image database in the recall phase to produce images with a natural appearance. To preserve the rich details of the NIR image, its high frequency features are transferred to the estimated RGB image. The presented approach is trained and evaluated on a real-world dataset containing a large amount of road scene images in summer. The dataset was captured by a multi-CCD NIR/RGB camera, which ensures a perfect pixel to pixel registration.