A Novel Approach for Colorization of a Grayscale Image using Soft Computing Techniques

Colorization of grayscale image is a process to convert a grayscale image into a color one. Few research works reported in literature on this but there is hardly any generalized method that successfully colorizes all types of grayscale image. This study proposes a novel grayscale image colorization method using a reference color image. It takes the grayscale image and the type of the query image as input. First, it selects reference image from color image database using histogram index of the query image and histogram index of luminance channel of color images of respective type. Once the reference image is selected, four features are extracted for each pixel of the luminance channel of the reference image. These extracted features as input and chrominance blue(Cb) value as target value forms the training dataset for Cb channel. Similarly training dataset for chrominance red(Cr) channel is also formed. These extracted features of the reference image and associated chrominance values are used to train two artificial neural network(ANN)- one for Cb and one for Cr channel. Then, for each pixel of the of query image, same four features are extracted and used as input to the trained ANN to predict the chrominance values of the query image. Thus predicted chrominance values along with the original luminance values of the query image are used to construct the colorized image. The experiment has been conducted on images collected from different standard image database i.e. FRAV2D, UCID.v2 and images captured using standard digital camera etc. These images are initially converted into grayscale images and then the colorization method was applied. For performance evaluation, PSNR between the original color image and newly colorized image is calculated. PSNR shows that the proposed method better colorizes than the recently reported methods in the literature. Beside this, “Colorization Turing test” was conducted asking human subject to choose the image (closer to the original color image) among the colorized images using proposed algorithm and recently reported methods. In 80% of cases colorized images using the proposed method got selected.