Using of convolutional neural network forgrayscale image colorization

The advancement of data technology had motivated more facilitation of human life including security and pattern recognition. Large information can be obtained from the image after proper feature extraction. Thus, the field of image processing has gained extended attention especially after incorporating artificial intelligence (AI). One of the vital problems that image processing is looking after is dealing with ancient images or in other words, the grayscale images which were captured before the introduction of RBG technology. Analyzing grayscale images for more curtail knowledge extraction is possible through colorizing technology where the grayscale image is converted into an RBG image. In this thesis, the development of an accurate image colorization approach is proposed using a convolutional neural network (CNN). Optimization of colorization performance is conducted through tuning of the CNN model. Results have shown that the proposed classifier have been scored of 80.91 Percent accuracy.