Colorization of Grayscale Images using Deep Convolution Neural Networks - Gudipalli, Abhishek; Sujatha, Canavoy Narahari; Prabha N., Amutha; Khanikhar, Krishna (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The work proposed in this paper is a high-performance colorization model that can be used in a wide range of applications such as colorization of old photos, restoration of damaged images, and even in the film and animation industry. It’s not always the intention of colorization to restore an image to its exact ground truth color. Instead, even if the colorization differs slightly from the actual colors, the goal is to create believable shading that is aesthetically pleasing and helpful to the user. We have used a range of deep learning techniques along with Convolutional Neural Networks (CNNs) to achieve our goals. With the use of vast datasets of colored images, our software facilitates the development and instruction of deep CNNs, which are capable of extracting pertinent characteristics and recognizing relationships between them. The resulting knowledge is then applied to the task of predicting accurate colorizations of grayscale images.