Automatic Sketch Colorization using DCGAN
Hwan Heo, Youngbae Hwang · International Conference on Control, Automation and Systems · 2018
In general, the manual coloring task from the black-white sketch is complicated and time-consuming. Furthermore, in the case of coloring which is a repetition of a similar pattern, it can be seen that manual coloring is less efficient. Therefore, the technique of automatically coloring from black-white sketch can become a practical application. We propose automatic sketch colorization by using U-Net and deep convolutional generative adversarial network (DCGAN) in the generative model. Experimental results on test set show various results including errors depend on test images.