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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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Image generation with a GAN

A generative adversarial network (GAN) is a generative model that defines an adversarial net framework and is composed of a couple of models (both models are CNNs in general), namely a generator and a discriminator, with the goal of generating new realistic images when given a set of training images. These two models act as adversaries of each other: the generator learns to generate new fake images that look like real images (starting with random noise) whilethe discriminator learns to determine whether a sample image is a real or a fake image.

The generator plays the role of a counterfeiter that is trying to produce a fake image and fool the discriminator, whereas the discriminator plays the role of the police that tries to detect the fake image generated by the discriminator. We can think of this as a two-player game and competition...

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