The coloring efficiency of most colorization algorithms is suboptimal. Therefore, the dual feature extractor generative adversarial network was designed for colorization. The U-Net-like network was used as the trunk network in the generator. The encoder was used to extract the local features of a grayscale image. The branch extractor used the ResNeXt network that was added to the SE module as a high-level feature extractor to extract the global features of the grayscale image. The two features were fused to predict the chrominance. This strategy prevented color leakage and detail loss in colorization. Moreover, the adversarial loss was added to minimize the tendency toward acquiring an unsaturated tone. The effectiveness of the proposed method was evaluated quantitatively and qualitatively. |
ACCESS THE FULL ARTICLE
No SPIE Account? Create one
![Lens.org Logo](/images/Lens.org/lens-logo.png)
CITATIONS
Cited by 3 scholarly publications.
RGB color model
Gallium nitride
Data modeling
Feature extraction
Computer programming
Image fusion
Convolution