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Python Image Processing Cookbook

By Sandipan Dey
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  1. Free Chapter
    Image Enhancement
About 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.
Publication date:
April 2020
Publisher
Packt
Pages
438
ISBN
9781789537147

 

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...
 

Applying filters to denoise different types of noise in an image

Noise represents random variations of image intensity that cause image quality to deteriorate. Noise can be introduced when the image is captured or transmitted. Image denoising (noise removal) is a vital image processing task that must be done for most of the image processing applications. In this recipe, we will discuss different types of noise with different distributions, such as Gaussian, Salt and Pepper, Speckle, Poisson, and exponential, and image denoising performed for different noise types with a couple of popular filtering techniques (mean and median filters), using the ndimage module from SciPy. The results will be compared for all types of noise.

Getting ready

...
 

Image denoising with a denoising autoencoder

An autoencoder is a neural network often used to learn an efficient representation of input data (typically in a reduced dimension) in an unsupervised way. A denoising autoencoder is a stochastic version of an autoencoder that takes (similar) inputs corrupted by noise and is trained to recover the original inputs (typically using some deep learning library functions) in order to obtain a good representation. We can use denoising autoencoders to learn robust representations from a set of similar input images (corrupted with noise) and then generate the denoised images.

Getting ready

We will be using the labeled faces in the wild (lfw) face dataset from scikit-learn (it contains face...

 

Image denoising with PCA/DFT/DWT

Principal component analysis (PCA), discrete Fourier transform (DFT), and discrete wavelet transform (DWT) are traditional machine learning techniques that can be used to denoise images as well. Each of these techniques will learn a representation (an approximation) of the image space and will retain mostly the information content in the images and remove the noise.

Getting ready

We will use the Olivetti faces dataset for this recipe. The dataset contains a total of 400 grayscale face images (each of size 64 x 64), 10 per each of the 40 objects. As usual, let's start by importing the required libraries:

import numpy as np
from numpy.random import RandomState
import matplotlib.pyplot as...
 

Image denoising with anisotropic diffusion

In this recipe, you will learn how to use the anisotropic (heat) diffusion equation to denoise an image preserving the edges by using a medpy library function. Isotropic diffusion, on the other hand, is identical to applying a Gaussian filter, which does not preserve the edges in an image, as we have already seen.

Getting ready

In this recipe, we will use the cameraman grayscale image. As usual, let's start by importing the required libraries:

from medpy.filter.smoothing import anisotropic_diffusion
from skimage.util import random_noise
from skimage.io import imread
from skimage import img_as_float
import matplotlib.pylab as plt
import numpyp as np

...
 

Improving image contrast with histogram equalization

In Chapter 1, Image Manipulation and Transformation, we saw how the contrast stretching operation can be used to increase the contrast of an image. However, it is just a linear scaling function that is applied to image pixel values, and hence the image enhancement is less drastic than its more sophisticated counterpart, histogram equalization. This recipe will show how to implement contrast stretching using the histogram equalization. It is also a point transform that uses a non-linear mapping that reassigns the pixel intensity values in the input image in such a way that the output image has a uniform distribution of intensities (a flat histogram), and thereby enhances the contrast of the image.

Getting ready

...
 

Implementing histogram matching

Histogram matching is an image processing task where an image is altered in such a way that its histogram matches the histogram of another reference (template) image's histogram. The algorithm is described as follows:

  1. Compute the cumulative histogram for each image.
  2. For any given pixel value, xi, in the input image, find the corresponding pixel value, xj, in the output image by matching the input image's histogram with the template image's histogram (G(xi)=H(xj), as shown in the following diagram.
  3. Replace pixel xi in the input with xj as shown in the following diagram:

In this recipe, we will implement histogram matching for colored images on our own.

Getting ready

As usual...

 

Performing gradient blending

The goal of Poisson image editing is to perform seamless (gradient) blending (cloning) of an object or a texture from a source image (captured by a mask image) with a target image. We want to create a photomontage by pasting an image region onto a new background using Poisson image editing. The idea is from the SIGGRAPH 2003 paper, Poisson Image Editing, by Perez et al., which shows that blending using the image gradients produces much more realistic results.

The gradient of the source and output images in the masked region will be the same after seamless cloning is done. Moreover, the intensity of the target image and the output image at the masked region boundary will be the same. The following diagram shows how a source image patch g is integrated seamlessly with a target image f* (over the region Ω), with a new image patch f (over the region...

 

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

Edge detection is a preprocessing technique where the input is typically a two-dimensional (grayscale) image and the output is a set of curves (that are called the edges). The pixels that construct the edges in an image are the ones where there are sudden rapid changes (discontinuities) in the image intensity function, and the goal of edge detection is to identify these changes. Edges are typically detected by finding the local extrema of the first derivative (gradient) or by finding the zero-crossings of the second derivative (Laplacian) of the image. In this recipe, we will first implement two very popular edge detection techniques, namely, Canny and Marr-Hildreth (LoG with Zero crossings). Then, we will implement wavelet-based edge detection.

...
About the Author
  • Sandipan Dey

    Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses. He is a regular blogger (sandipanweb) and is a machine learning education enthusiast.

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