Automatic image colorization using segmentation based on pixon and fuzzy logic theory

Colorization is the process of color allocation into grayscale images or films. Since several colors are the same level of illumination, the colorization of a grayscale image into a colorized one has not a unique and automatic solution, and the human has a cardinal role in the colorization process. In other hand, in a grayscale image, the information of the neighbor pixels of a pixel are effective in its color. In this paper, a novel idea has been proposed for colorization of a grayscale image based on image segmentation, with minimum human interference. The proposed method use a collection of reference images consist of some classes of natural color images. Some example of classes are tree, mountain, jungle, sea, human flower and etc. As soon as the user import a test grayscale image into the algorithm, its type (class) is selected by the user. The user can select more than one class for a test image. The reference images of the selected class are used as the reference images in the colorization process. On the other hand, the test image is segmented, and for each segment, the most similar segment in the set of the segments of the selected reference images is specified. The segment of the grayscale test image is colorized using the fuzzy theory based on the specified segment of the reference image. This process is done for all the segments of the test image. Finally, a post process is applied to match the color of the neighbor pixels. The minimum human interference and the use of the information of the neighbor pixels are the most important advantages of the propose method.