What do you get with Print?

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

Black & white paperback book shipped to your 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

Buy Now

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...

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.

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?