Epitome for Automatic Image Colorization
This addresses the need for automated colorization to enhance visual appeal and information in images, particularly for scientific applications, but it appears incremental as it builds on existing epitome techniques.
The paper tackles the problem of automatic image colorization without user interaction by developing a method using epitome, a generative graphical model, which renders better results than previous methods in experiments.
Image colorization adds color to grayscale images. It not only increases the visual appeal of grayscale images, but also enriches the information contained in scientific images that lack color information. Most existing methods of colorization require laborious user interaction for scribbles or image segmentation. To eliminate the need for human labor, we develop an automatic image colorization method using epitome. Built upon a generative graphical model, epitome is a condensed image appearance and shape model which also proves to be an effective summary of color information for the colorization task. We train the epitome from the reference images and perform inference in the epitome to colorize grayscale images, rendering better colorization results than previous method in our experiments.