Book Image

Hands-On Image Processing with Python

By : Sandipan Dey
Book Image

Hands-On Image Processing with Python

By: Sandipan Dey

Overview of this book

Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python. The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing. By the end of this book, we will have learned to implement various algorithms for efficient image processing.
Table of Contents (20 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Variational image processing


In this section, we shall very briefly discuss variational methods in image processing, with an example application in denoising. Image processing tasks can be viewed as function estimation (for example, segmentation can be thought of as finding a smooth closed curve between an object and the background). Calculus of variations can be used for minimization of the appropriately defined energy functionals (with the Euler-Langrange method) for a specific image processing task, and the gradient descent method is used to evolve towards the solution.

The following diagram describes the basic steps in an image processing task, represented as a variational optimization problem. First, we need to create an energy functional E that describes the quality of the input image u. Then, with the Euler-Lagrange equation, we need to calculate the first variation. Next, we need to set up a partial differentail equation (PDE) for the steepest descent minimization and discretize it...