Fundamental
Image Statistics: illuminance, color temperature, saturation, local contrast, hue, texture, tone
Color spaces: RGB color space, CIELab color space (saturation/chrominance, hue, luminance).
Image realism
Predict the realism using the discriminator learnt based on real images and fake images [a]
Predict the realism based on global and local statistics: distance to neighboring realistic image, similarity between foreground and background [a]
Image harmonization
After pasting the foreground on the background, harmonize the foreground.
- Traditional methods: match the foreground with the background; match the foreground with other semantically or statistically close realistic images.
- Deep learning methods: https://github.com/bcmi/Awesome-Image-Harmonization
One interesting problem in image harmonization is whether the decomposition of reflectance and illumination is unique. If we have strong prior knowledge for the object reflectance (e.g., black-and-white zebra), the decomposition may be unique. Or if the object color is complex enough, which is equivalent to adding enough constraints, the decomposition may be unique. Otherwise, if we do not have strong prior knowledge for the object reflectance (e.g., a vase of arbitrary color) and the object color is simple (e.g., a single color), the decomposition is not unique.
Given a source image and an obtained target image after applying color transfer, we hope to know whether there exists a valid path between source image and target image and whether there exist multiple valid paths between them.
Deep painterly harmonization
Reference
[1] Luan, Fujun, et al. “Deep painterly harmonization.” Computer graphics forum. Vol. 37. No. 4. 2018.
[2] Peng, Hwai-Jin, Chia-Ming Wang, and Yu-Chiang Frank Wang. “Element-Embedded Style Transfer Networks for Style Harmonization.” BMVC. 2019.
[3] Zhang, Lingzhi, Tarmily Wen, and Jianbo Shi. “Deep image blending.” WACV. 2020.