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Hands-On Image Processing with Python

Hands-On Image Processing with Python

By : Sandipan Dey
3 (5)
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Hands-On Image Processing with Python

Hands-On Image Processing with Python

3 (5)
By: Sandipan Dey

Overview of this book

Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python. The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing. By the end of this book, we will have learned to implement various algorithms for efficient image processing.
Table of Contents (20 chapters)
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Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
2
Index

Chapter 4. Image Enhancement

In this chapter, we will discuss some of the most basic tools in image processing, such as mean/median filtering and histogram equalization, which are still among the most powerful tools. The objective of image enhancement is to improve the quality of an image or make particular features appear more prominent. These techniques are more general purpose techniques, and a strong model of the degradation process is not assumed (unlike image restoration). Some examples of image enhancement techniques are contrast stretching, smoothing, and sharpening. We will describe the basic concepts and implementation of these techniques using Python library functions and using PIL, scikit-image, and scipy ndimage libraries. We will become familiar with simple and still-popular approaches.

We will start with point-wise intensity transformation, and then discuss contrast stretching, thresholding, half-toning, and dithering algorithms, and the corresponding Python library functions...

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