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.
Limmer and Lensch REF proposed a method for transferring the RGB color spectrum to near-infrared images using deep multi-scale convolutional neural networks.
Infrared Colorization Using Deep Convolutional Neural Networks
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
Computer Science