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

Convolution theorem and frequency domain Gaussian blur


In this section, we will see more applications of convolution on images using Python modules such as scipy signal and ndimage. Let's start with convolution theorem and see how the convolution operation becomes easier in the frequency domain.

Application of the convolution theorem

The convolution theorem says that convolution in an image domain is equivalent to asimple multiplication in the frequency domain:

Following diagram shows the application of fourier transforms:

The next diagram shows the basic steps in frequency domain filtering. We have the original image, F, and a kernel (a mask or a degradation/enhancement function) as input. First, both input items need to be converted into the frequency domain with DFT, and then the convolution needs to be applied, which by convolution theorem is just an (element-wise) multiplication. This outputs the convolved image in the frequency domain, on which we need to apply IDFT to obtain the reconstructed...