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Color is a key attribute of an image due to its extensive use in various important fields like medical image processing, face image processing, video processing, entertainment etc. (Gonzalez et al., 2010; Umbaugh, 1998; Gonzalez & Woods, 2010). Colorization is a process that converts a gray scale image to a color one. It is an active and challenging area of research with a lot of interest in the image editing and compression community (Zhang et al., 2016; Larsson et al., 2016; Haldankar et al., 2007; Reinhard et al., 2001; Welsh et al., 2002; Blasi et al., 2003; Levin et al., 2004; Yatziv & Sapiro, 2006; Kang & March, 2007; Sathik & Parveen, 2010; Bugeau et al., 2014; Hasnat et al., 2017). A few grayscale image colorization methods work successfully on different types of images. Mainly, two types of colorization methods are found in the literature. In the first category, the user manually colorizes some sample area in the grayscale image which is later used as source of color for colorization of the entire grayscale image. But this process is time consuming and tedious. Also, it requires very careful sample color selection from the user. The second category of algorithms uses a reference color image which presumably contains semantically similar color for the grayscale query image. This process is semi-automatic and sometimes requires user intervention in the process and has produced some impressive colorization using user input. But sufficiently complex images still may require many user interactions.
Colorization process has no unique solution because for a same luminance value in two different positions in the grayscale image, there may be different chrominance value in the color image. Although some works found in the literature, colorization still remains a challenging area. Haldankar et al. (2007) proposed a system in 2007 which modifies a gray scale image into a color one by the luminance effect of the reference image but the time required for colorization is huge for a large size image and hence the method is less effective in real time system. In 2001, Reinhard et al. (2001) proposed a method for a general color correction that takes one image's color characteristics from another using statistical analysis to make a synthetic image which takes on another image's look. But this method is not actually a method of grayscale image colorization. A semi-automatic neighborhood statistics based colorization approach was proposed by Welsh et al. (2002). Their method, effectively colorizes a grayscale image where they choose to transfer only chromatic information and retain the original luminance values of the query image. Further rectangular swatch based colorization is done for enhancing the colorized areas where the user is not satisfied with the color information. In this method, colors are transferred between the corresponding swatches. But Welsh et. al (2002) finally concluded that their method won’t work on images where color change is gradual such as face image. Moreover, all these existing methods take reasonable amount of time for colorization.