Deep Colorization
Shanghai Jiao Tong University · City University of Hong Kong
Abstract
This paper investigates into the colorization problem which converts a grayscale image to a colorful version. This is a very difficult problem and normally requires manual adjustment to achieve artifact-free quality. For instance, it normally requires human-labelled color scribbles on the grayscale target image or a careful selection of colorful reference images (e.g., capturing the same scene in the grayscale target image). Unlike the previous methods, this paper aims at a high-quality fully-automatic colorization method. With the assumption of a perfect patch matching technique, the use of an extremely large-scale reference database (that contains sufficient color images) is the most reliable solution to the…
Citation impact
- FWCI
- 19.84
- Percentile
- 100%
- References
- 39
Authors
3Topics & keywords
- Grayscale
- Computer science
- Artificial intelligence
- Computer vision
- Artifact (error)
- Noise (video)
- Matching (statistics)
- Image (mathematics)