The aim of this project is to automatically add color to grayscale images using an autoencoder architecture. The process involves several steps, including data preparation, defining the model architecture, training the model, and deploying it for practical use.

This project implements a Convolutional Autoencoder for image colorization. The model is designed to take grayscale images as input and generate corresponding colorized versions. It utilizes deep learning techniques, specifically convolutional neural networks, to learn the mapping between grayscale and color images.

Three autoencoders models and one ColorGAN model on a landscape image dataset to colorize black-and-white images to color images are implemented. The two color space chosen for colorization is RGB and CIELAB.

Sep 30, 2021 · Image Colorization is the problem of defining colors for grayscale images. Recently many research works have been conducted to propose fully-automatic colorization methods.

Apr 17, 2020 · Autoencoders are a type of Neural Networks’ architecture that is similar to techniques such as the Principal Component Analysis – they both try to reduce the dimensionality of the input data. However, as we will see, autoencoders can do much more. They are built from two components:

Automatic image colorization often involves the use of a class of convolutional neural networks (CNN) called autoencoders. These neural networks are able to distill the salient features of an image, and then regenerate the image based on these learned features.

This paper proposes and implements a deep convolutional autoencoder architecture that maximizes the image colorization performance on two different datasets, the Fruit-360 and Flickr-Faces-HQ. To this end, a modification of the VGG16 model and a custom deep CNN model were assembled to predict and portray colors on grayscale images.

This paper explores the application of autoencoders for image colorization. The purpose of the paper is to develop a model that can accurately predict the color of a grayscale image. We trained the autoencoder on a dataset of grayscale images and their corresponding colored images.

It takes grayscale images as input and outputs colorized versions of those images. The model architecture consists of an encoder-decoder structure, where the encoder compresses the input image into a latent representation, and the decoder reconstructs the image in color.

Convolutional autoencoder neural network and generative adversarial network (GAN) have been implemented on black and white images. The networks aim to convert the black and white images to their respective color format. The color format used for the implementation of the networks is RGB color space.