Colourization of Grayscale Images Using Deep Learning

Abstract

We propose a deep learning approach for image colourization. The system directly maps a grayscale image to an output colourization using a neural network. The network propagates through low level cues and high level semantics information, which it learns from large scale dataset. One can easily find similar projects present in the web, but in our project we will be having a framework which can incorporate user hints, as to give desired colourization. We know that in a black-white image a red object and a blue object will appear similar of the shade of black. Thus many a time the colour put onto the object by the algorithm might not be similar to what we are used of seeing around. In this case history and prior knowledge of the image is necessary for accuracy and helps the user to get image which is in accordance to the perception of the user for that image. The neural network which will be taught to add colour to a grey scale image with the right gradient of colour for specific objects based on nature. We will be providing simple images to the neural network at first so as to identify simple objects and understand the colours involved with those objects. Thus when we pass on an image comprising of various different objects, then the neural network is responsible of identifying each of the unique objects in that image and add colour to it. The key features of this project would be to get a medium-tohigh level accuracy of colourization of a given image. The algorithm would add colour to an image in real time without much latency and delay involved.