Deep Learning in Parallel Automatic Colorization of Black and White Images

Abstract

Image colorization is defined as the problem of assigning colors for grayscale images. Due to the success of applying Deep Learning techniques in different applications, it is used to solve the colorization problem. Adopting Deep Learning in image colorization proved to be a promising approach that might show future breakthroughs. Image colorization solutions can be classified according to several criteria as input image type, number of colored output images, colorization methodology, techniques or networks used in colorization and network paths. However, most of the solutions are classified in literature according to one or two criteria.

The thesis presents a solid review that targets all these criteria to classify the solutions simultaneously with a considerably large number of papers. Additionally, the review shows the most commonly used measuring metrics of comparison as well as the used datasets. The findings of the review reveal that Deep Learning has become a widely used approach in solving the colorization problem.

Despite the large number of papers that solved the fully automatic colorization problem, many of them failed to accurately colorize images with several objects. The reason behind this could be dealing with the multi-objects in the image as a single whole image, regardless the variety of