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 PCA/DFT/DWT

Principal component analysis (PCA), discrete Fourier transform (DFT), and discrete wavelet transform (DWT) are traditional machine learning techniques that can be used to denoise images as well. Each of these techniques will learn a representation (an approximation) of the image space and will retain mostly the information content in the images and remove the noise.

Getting ready

We will use the Olivetti faces dataset for this recipe. The dataset contains a total of 400 grayscale face images (each of size 64 x 64), 10 per each of the 40 objects. As usual, let's start by importing the required libraries:

import numpy as np
from numpy.random import RandomState
import matplotlib.pyplot as...