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Python Image Processing Cookbook

Python Image Processing Cookbook

By : Sandipan Dey
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Python Image Processing Cookbook

Python Image Processing Cookbook

2 (2)
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)
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Restoring an image with the Wiener filter

The Wiener filter is Mean Squared Error (MSE) filtering that incorporates both the degradation function and the statistical characteristics of noise. The underlying assumption is that the noise and image are uncorrelated. It optimizes the filter so that MSE is minimized. In this recipe, you will learn how to implement the Wiener filter using functions from scikit-image restoration module and how to apply the filter to restore a degraded image, both in a supervised and unsupervised manner.

Getting ready

In this recipe, we shall use a cactus image as input and corrupt it with noise/blur. As usual, let's first import all of the required libraries using the following lines of code...

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