Paper Contents
Abstract
ABSTRACT we propose a fully automatic image colorization method for grayscale images. Image colorization algorithms aim to add realistic colorized to grayscale images. They typically leverage deep learning models, such as convolutional neural networks (CNNs), trained on large datasets. These models learn mappings between grayscale and colorized images, capturing complex relationships. Utilizing techniques like auto encoders or GANs, they generate colorization predictions based on input grayscale features. The process involves extracting features, mapping them to colorization and refining predictions iteratively. Dataset diversity, model architecture, and training strategies play crucial roles in achieving accurate and visually appealing colorizations. This research paper includes various research on image colorization.
Copyright
Copyright © 2024 Ranjitha j. This is an open access article distributed under the Creative Commons Attribution License.
