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 anisotropic diffusion

In this recipe, you will learn how to use the anisotropic (heat) diffusion equation to denoise an image preserving the edges by using a medpy library function. Isotropic diffusion, on the other hand, is identical to applying a Gaussian filter, which does not preserve the edges in an image, as we have already seen.

Getting ready

In this recipe, we will use the cameraman grayscale image. As usual, let's start by importing the required libraries:

from medpy.filter.smoothing import anisotropic_diffusion
from skimage.util import random_noise
from import imread
from skimage import img_as_float
import matplotlib.pylab as plt
import numpyp as np