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 denoising with a denoising autoencoder

An autoencoder is a neural network often used to learn an efficient representation of input data (typically in a reduced dimension) in an unsupervised way. A denoising autoencoder is a stochastic version of an autoencoder that takes (similar) inputs corrupted by noise and is trained to recover the original inputs (typically using some deep learning library functions) in order to obtain a good representation. We can use denoising autoencoders to learn robust representations from a set of similar input images (corrupted with noise) and then generate the denoised images.

Getting ready

We will be using the labeled faces in the wild (lfw) face dataset from scikit-learn (it contains face...