- Plot the frequency spectrum of an image, a Gaussian kernel, and the image obtained after convolution in the frequency domain, in 3D (the output should be like the surfaces shown in the sections) using the
mpl_toolkits.mplot3d
module. (Hint: thenp.meshgrid()
function will come in handy for thesurface
plot). Repeat the exercise for the inverse filter too. - Add some random noise to the
lena
image, blur the image with a Gaussian kernel, and then try to restore the image using an inverse filter, as shown in the corresponding example. What happens and why? - Use SciPy signal's
fftconvolve()
function to apply a Gaussian blur on a color image in the frequency domain. - Use the
fourier_uniform()
andfourier_ellipsoid()
functions of thendimage
module of SciPy to apply LPFs with box and ellipsoid kernels, respectively, on an image in the frequency domain.
Hands-On Image Processing with Python
By :
Hands-On Image Processing with Python
By:
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)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Free Chapter
Getting Started with Image Processing
Sampling, Fourier Transform, and Convolution
Convolution and Frequency Domain Filtering
Image Enhancement
Image Enhancement Using Derivatives
Morphological Image Processing
Extracting Image Features and Descriptors
Image Segmentation
Classical Machine Learning Methods in Image Processing
Deep Learning in Image Processing - Image Classification
Deep Learning in Image Processing - Object Detection, and more
Additional Problems in Image Processing
Other Books You May Enjoy
Index
Customer Reviews