Book Image

Hands-On Image Processing with Python

By : Sandipan Dey
Book Image

Hands-On Image Processing with Python

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)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Linear noise smoothing


Linear (spatial) filtering is a function with a weighted sum of pixel values (in a neighborhood). It is a linear operation on an image that can be used for blurring/noise reduction. Blurring is used in pre-processing steps; for example, in the removal of small (irrelevant) details. A few popular linear filters are the box filter and the Gaussian filter. The filter is implemented with a small (for example, 3 x 3) kernel (mask), and the pixel values are recomputed by sliding the mask over the input image and applying the filter function to every possible pixel in the input image (the input image's center pixel value corresponding to the mask is replaced by the weighted sum of pixel values, with the weights from the mask). The box filter (also called the averaging filter), for example, replaces each pixel with an average of its neighborhood and achieves a smoothing effect(by removing sharp features; for example, it blurs edges, whereas spatial averaging removes noise...