An Overview of the Most Important Methods for Coloring Grayscale Images

Abstract

Colorization: Converting a grayscale image into an image with colors acceptable to the viewer and achieving the minimum coloration goal until it becomes closer to reality. The goal of the coloring process is to produce a convincing image and the result is close to the reality of the colored image. Converting grayscale images to color images is a very difficult process due to the specificity of grayscale images and how closely they match color in every detail of the image. Coloring methods have evolved to become less invasive and produce more acceptable results to the viewer's eye, but they have become more difficult. This research provides an overview of image colorization methods, their advantages and disadvantages, and compares the quality of each method according to specific criteria. Coloring methods have evolved from primitive methods based on the manual principle to using computing techniques such as adding scribbles within areas of the image, then moving to choosing a reference image that can be used to obtain colors, to using deep learning techniques that rely on training a model on colored images and then testing this model and showing Methods based on deep learning have better results than other methods, and the algorithm showed GAN outstanding results in coloring that outperform some deep learning coloring methods, and also outperform other methods. We hope that this study will be a scientific product that will benefit the research community and enable them to obtain a useful overview of the most important methods of coloring images.