Image Colorization Using Deep Learning (Part 2)

Today, colorization is done by hand in Photoshop. A picture can take up to one month to colorize. It requires extensive research. A face alone needs up to 20 layers of pink, green and blue shades to get it just right. Coloring grayscale images manually is a slow and hectic process. Using machine learning techniques, this can be done very fast. Previous approaches to black and white image colorization relied on manual human annotation and often produced desaturated results that were not “believable” as true colorizations. The main objective is to colourize grayscale images, and this can be done using Deep Learning and Convolutional Neural Networks. Convolutional neural networks have emerged as a standard in image classification problems. They achieve higher accuracy rates over all the techniques in detecting patterns in images. As this problem mostly deals with identifying the pattern in the image and colourizing it accordingly, convolutional neural networks serve the best. The primary application is colourisation of black and white images and can be useful in Cyber Forensics as well. As of late, CNNs have risen as the true standard for tackling picture grouping issues, accomplishing blunder rates lower than 4% in the ImageNet challenge. This project will be using Python and OpenCV to derive the result. CNNs owe a lot of their prosperity to their capacity to learn also, perceive hues, examples, and shapes inside pictures and partner them with object classes. We accept that these qualities normally lend themselves well to colourizing pictures since object classes, examples, and shapes for the most part connect with the shading decision.