Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICMLA.2016.0019 | 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) |
Keywords | DocType | Volume |
Neural Networks,DNN,CNN,Multi-scale,NIR,Near Infrared,Colorization,Bilateral Filter | Conference | abs/1604.02245 |
ISBN | Citations | PageRank |
978-1-5090-6168-6 | 7 | 0.62 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Limmer | 1 | 8 | 1.33 |
| Hendrik P. A. Lensch | 2 | 1471 | 96.59 |