Image Super-Resolution using CNN with Keras in Python
Enhance/Upsample Images with Convolutional Neural Network for Computer Vision With TensorFlow on Google Colab : Hands-on
What you will learn
Understand the fundamentals of Efficient Sub-pixel Convolutional Neural Network (CNN)
Build and train a the super-resolution model using Keras with Tensorflow as a backend using Google Colab
Assess the performance of trained model
Learn to use the trained model to predict the high-resolution image of a new set of image data
Why take this course?
Welcome to the "Image Super-Resolution using CNN with Keras in Python" course. In this project, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend from scratch, and you will learn to train CNNs to enhance the quality of images significantly. Our neural network will create high-resolution images from low-resolution images. Please note that you don't need a high-powered workstation to learn this course. We will be carrying out the entire project in the Google Colab environment, which is free. You only need an internet connection and a free Gmail account to complete this course. This is a practical course, we will focus on Python programming, and you will understand every part of the program very well. By the end of this course, you will be able to build and train the deep learning model using your image dataset. After that, you will also be able to use the model to predict high-resolution images on new images and visualise them. This image super-resolution course is practical and directly applicable to many industries. You can add this project to your portfolio of projects which is essential for your following job interview. This course is designed most straightforwardly to utilise your time wisely.
Happy learning.
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