This project uses deep learning to automatically colorize grayscale images, restoring realistic and visually coherent colors.
The main goal of this project is to develop an AI model capable of transforming grayscale images into their colorized counterparts. The aim is to model the complex relationship between luminance (grayscale intensity) and plausible color distributions using deep learning techniques. By learning from large datasets of color images, the model can predict realistic and visually appealing color versions of black-and-white inputs.
This project showcases the power of AI in creative image processing tasks and explores techniques such as convolutional neural networks (CNNs), image-to-image translation, and loss function optimization to achieve high-quality colorization results.
The dataset is sourced from Kaggle, and the model will be trained using convolutional neural networks (CNNs), with support from frameworks such as PyTorch and OpenCV for preprocessing, training, and image manipulation.
The choice of dataset, model architecture, and supporting tools is subject to change as the project develops and new insights are gained.
License to be determined. This project is currently in development and licensing details may change.