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'll 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
Requirements
Basic knowledge of Python Programming
Description
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.

Overview

Section 1: Fundamentals

Lecture 1 Introduction

Lecture 2 Artificial Intelligence

Lecture 3 Machine Learning

Lecture 4 Deep Learning

Lecture 5 What is Image Super-Resolution?

Lecture 6 How Image Super-Resolution is done?

Lecture 7 Efficient Sub-pixel Convolutional Neural Network (ESPCN)

Section 2: Building, Evaluating and Predicting Super-Resolution Model

Lecture 8 Download Dataset

Lecture 9 What is inside data folder?

Lecture 10 Super-Resolution Python Code

Lecture 11 What is the .h5 file?

Lecture 12 What is inside test folder?

Lecture 13 What is inside prediction folder?

Lecture 14 Enabling GPU in Google Colab

Lecture 15 Is GPU connected to Colab notebook?

Lecture 16 Connect Google Colab with Google Drive

Lecture 17 Import Python Libraries

Lecture 18 Creating Training Data Generator

Lecture 19 Creating Validation Data Generator

Lecture 20 Normalize the Pixels for Training and Validation Images

Lecture 21 Visualize Sample Images

Lecture 22 Process the Input Images and Visualize it

Lecture 23 Build a CNN Model Architecture

Lecture 24 Define Utility Functions to Monitor our Results

Lecture 25 Peak Signal to Noise Ratio (PSNR)

Lecture 26 Dataset of Test Image Paths

Lecture 27 Define Callbacks

Lecture 28 Visualize Model Architecture

Lecture 29 Model Compilation

Lecture 30 Training the Model

Lecture 31 Model Testing / Evaluation

Lecture 32 Prediction

Beginners starting out to the field of Deep Learning,Industry professionals and aspiring data scientists,People who want to know how to write their image super-resolution code