Gray to Glow: Colorize Grayscale Images Using Deep Learning

The automatic colorization of grayscale images is a difficult computer vision problem because there are multiple possible colorizations for each grayscale image. To solve this issue, the Gray to Glow project aims to create an automatic deep learning-based system which transforms a grayscale image into a color image without the need for user scribbles or reference images, or manual intervention. The proposed system is based on the use of a CNN for color prediction in the CIE Lab color space, leaving the L channel unchanged and estimating the missing channels in a classification oriented way that minimizes the number of desaturated outputs while maximizing color vividness. It combines pretrained ECCV16 and SIGGRAPH17 colorization models, image preprocessing, output reconstruction, and it has a Flask web app to make it accessible for both command-line and browser. Experimental results presented in the thesis confirm that the system could produce visually meaningful colorized outputs, it was able to process outputs that were stable, and it was easy for users to use the system in enhancing grayscale images. The project illustrates how a deep learning model can be applied to develop a real-world and deployable system capable of image colourisation for image restoration, digital archiving and media enhancement applications. A key point is that computer vision applications can be extremely useful in colorizing images that are previously only in grayscale.One of the interesting aspects is that an image, when it is only in grayscale, can be very helpful in computer vision for image colorization, and it can be done very efficiently with deep learning techniques.