Book Image

Python Image Processing Cookbook

By : Sandipan Dey
Book Image

Python Image Processing Cookbook

By: Sandipan Dey

Overview of this book

With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing. With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems. By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.
Table of Contents (11 chapters)

Image restoration with a Markov random field

In this recipe, we shall discuss how a Markov random field (MRF) can be used to denoise an image. Let's say we have a noisy binary image, X, with pixel values Xij ∈ {-1, +1} and we want to recover the noiseless image, Y. If the amount of noise is assumed to be small, there will be a good correlation between a pixel in X and the corresponding pixel in Y and in a 4-connected neighborhood, pixels of X will be well-correlated. This can be modeled as an MRF as shown in the following diagram, with the total energy function that we shall like to minimize:

Getting ready

In this recipe, we will use the cameraman grayscale image and we will corrupt the image with noise. Next...