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)

Applying filters to denoise different types of noise in an image

Noise represents random variations of image intensity that cause image quality to deteriorate. Noise can be introduced when the image is captured or transmitted. Image denoising (noise removal) is a vital image processing task that must be done for most of the image processing applications. In this recipe, we will discuss different types of noise with different distributions, such as Gaussian, Salt and Pepper, Speckle, Poisson, and exponential, and image denoising performed for different noise types with a couple of popular filtering techniques (mean and median filters), using the ndimage module from SciPy. The results will be compared for all types of noise.

Getting ready

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