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Image Enhancement

The objective of image enhancement is to improve the quality of an image or make particular features appear more prominent. The techniques used are often more general-purpose techniques and a strong model of the degradation process is not assumed (unlike image restoration, which we will see in the next chapter). Some examples of image enhancement techniques are denoising/smoothing (using different classical image processing, unsupervised machine learning, and deep learning techniques), contrast improvement, and sharpening.

In this chapter, we will cover the following recipes for image enhancement (and their implementations using Python libraries):

Applying filters to denoise different types of noise in an image
Image denoising with a denoising autoencoder
Image denoising with PCA/DFT/DWT
Image denoising with anisotropic diffusion
Improving image contrast with...

Key benefits

Discover solutions to complex image processing tasks using Python tools such as scikit-image and Keras

Learn popular concepts such as machine learning, deep learning, and neural networks for image processing

Explore common and not-so-common challenges faced in image processing

Description

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.

Who is this book for?

This book is for image processing engineers, computer vision engineers, software developers, machine learning engineers, or anyone who wants to become well-versed with image processing techniques and methods using a recipe-based approach. Although no image processing knowledge is expected, prior Python coding experience is necessary to understand key concepts covered in the book.

What you will learn

Implement supervised and unsupervised machine learning algorithms for image processing

Use deep neural network models for advanced image processing tasks

Perform image classification, object detection, and face recognition

Apply image segmentation and registration techniques on medical images to assist doctors

Use classical image processing and deep learning methods for image restoration

Implement text detection in images using Tesseract, the optical character recognition (OCR) engine

Understand image enhancement techniques such as gradient blending

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Guilherme Alessandri Alvim Miotto Jul 22, 2021

The recipes are cool, but the book has a poor graphical quality.The images are black and white and at very low resolution. Many of them are just useless, because they are so bad that it is not possible to appreciate the effect of the processing technique being demonstrated.Another serious problem are with the equations. They are not properly type-setted (e.g. with LaTeX) but instead they are printed as images, and since images are really bad in this book, the equations are sometimes unreadable.Now, one could say that the image quality is poor to keep the price of the book low. OK, fair enough. However, there is no excuse to not typeset the equations using a decent tool. This is just disregard with quality.

Amazon Verified review

Jan Feb 20, 2021

As a engineer I was looking for a collection of algorithms for computer vision. The table of content has promissing chapters, but stays far behind its depth. The book is only a cookbook which does not go on any details.Pros+ clearly structured content+ compact chaptersCons- Lots of different framework- aweful printing quality (As a book for vision, the picture have not enough resolution to see all or even any details of the algorithm output)- Huge font size (distracting and just lengthens the page numbers)- unexplained figures which just fell that they fill the pages- the "How it works" does not explain the algorithms -> It just rexplains what was done- no colored figures (Can be downloaded form https://static.packt-cdn.com/downloads/9781789537147_ColorImages.pdf)- does not compare the results of different algorithms- does not explain when to use or not to use a certain algorithms- uses a lot of signal procssing terms without giving any backgroundI overall do not recommend to buy the book as a printed format. I will use this book as a refence for quickly finding a python implementation of image processing tool and to see whichs algorithms are available. For a deeper understanding and testing, you have to look somewhere else.

Python Image Processing Cookbook: Over 60 recipes to help you perform complex image processing and computer vision tasks with ease

What do you get with Print?

Python Image Processing Cookbook

Image Enhancement

Applying filters to denoise different types of noise in an image

Getting ready

Image denoising with a denoising autoencoder

Getting ready

Image denoising with PCA/DFT/DWT

Getting ready

Image denoising with anisotropic diffusion

Getting ready

Improving image contrast with histogram equalization

Getting ready

Implementing histogram matching

Getting ready

Performing gradient blending

Edge detection with Canny, LoG/zero-crossing, and wavelets

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