Infrared Colorization Using Deep Convolutional Neural Networks
This addresses the challenge of enhancing NIR images for applications like surveillance or autonomous driving, though it is incremental as it builds on existing deep learning methods for image colorization.
The paper tackles the problem of colorizing near-infrared (NIR) images by transferring RGB color spectra using deep convolutional neural networks, resulting in images with a natural appearance without requiring user guidance or reference databases.
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.