In this recipe, you will learn how to use wavelets to transform an image and discard the lower-order bits from the output of the transform, so that most of its values are zero (or very small), but most of the signal (pixels) is preserved. We shall use the mahotas library functions for the demonstration.
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Python Image Processing Cookbook
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Python Image Processing Cookbook
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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)
Preface
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Image Manipulation and Transformation
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Image Enhancement
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Image Restoration
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Binary Image Processing
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Image Registration
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Image Segmentation
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Image Classification
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Object Detection in Images
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Face Recognition, Image Captioning, and More
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