Senior Data Scientist, Mawdoo3 Ltd
Machine Learning Manager, Adobe
Data Scientist, Inmobi
Data Scientist, Boeing
Image Processing Project -Train a model for colorization to make grayscale images colorful using convolutional autoencoders.
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Business Objective
Image colorization is the process of taking an input grayscale (black and white) image and then producing an output colorized image that represents the semantic colors and tones of the input. In image colorization, a color is assigned to each pixel of a target grayscale image.
Image colorization technique is helpful for many applications such as Medical Microscope, Medical Imagery, Denoising and recreating old Images, Night Vision Camera, etc.
In our use case, we will be using a fully automated data-driven technique called autoencoders for image colorization. Autoencoders are a specific type of feedforward neural network where the input is the same as the output. The VGG16 model will be used as a feature extractor. VGG16 is a classic neural network used as a backbone for many computer vision tasks
This project aims to build a convolutional neural network that will best convert the grayscale images to RGB images.
Data Description
Here, we will be using a landscape image dataset which consists of approximately,
Aim
To build a model using keras, that can best convert a grayscale image to a coloured image.
Tech stack
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