1.Deep Colorization
2.Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution
3.Learning Large-Scale Automatic Image Colorization
1.Fully Automatic Video Colorization With Self-Regularization and Diversity
2.Coloring With Limited Data Few Shot Colorization via Memory Augmented Networks
3.Deep Exemplar-Based Video Colorization
1.Colorization as a Proxy Task for Visual Understanding
2.Image-to-Image Translation with Conditional Adversarial Networks
3.Learning Diverse Image Colorization
4.Scribbler-Controlling Deep Image Synthesis with Sketch and Color
1.Coloring with Words Guiding Image Colorization Through Text-based Palette Generation
2.Structural Consistency and Controllability for Diverse Colorization
1.Colorful Image Colorization
2.Generative Visual Manipulation on the Natural Image Manifold
3.Learning Representations for Automatic Colorization
1.Automatic image colorization via multimodal predictions
1.Deep Examplar-Based Colorization
1.Real-Time User-Guided Image Colorization with Learned Deep Priors
1.Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification
1.Semantic Colorization with Internet Images
1.Manga Colorization
1.Colorization using Optimization
1.Transferring Color to Greyscale Images
1.Two-stage Sketch Colorization
1.Intrinsic Colorization
1.Natural Image Colorization
1.Colorization by example
1.Fast image and video colorization using chrominance blending
1.Color transfer between images
2.Image analogies
3.Normalized Cuts and Image Segmentation
1.Fully Automatic Video Colorization With Self-Regularization and Diversity
2.Deep Exemplar-Based Video Colorization
3.Fast image and video colorization using chrominance blending