Image Processing Masterclass with Python : 50+ Solutions and Techniques

Over 50 problems solved with classical algorithms + ML / DL models KEY FEATURES ● Problem-driven approach to practice image processing. ● Practical usage of popular Python libraries: Numpy, Scipy, scikit-image, PIL and SimpleITK. ● End-to-end demonstration of popular facial image processing challenges using MTCNN and Microsoft’s Cognitive Vision APIs. DESCRIPTION This book starts with basic Image Processing and manipulation problems and demonstrates how to solve them with popular Python libraries and modules. It then concentrates on problems based on Geometric image transformations and problems to be solved with Image hashing. Next, the book focuses on solving problems based on Sampling, Convolution, Discrete Fourier transform, Frequency domain filtering and image restoration with deconvolution. It also aims at solving Image enhancement problems using different algorithms such as spatial filters and create a super resolution image using SRGAN. Finally, it explores popular facial image processing problems and solves them with Machine learning and Deep learning models using popular python ML / DL libraries. WHAT YOU WILL LEARN ● Develop strong grip on the fundamentals of Image Processing and Image Manipulation. ● Solve popular Image Processing problems using Machine Learning and Deep Learning models. ● Working knowledge on Python libraries including numpy, scipy and scikit-image. ● Use popular Python Machine Learning packages such as scikit-learn, Keras and pytorch. ● Live implementation of Facial Image Processing techniques such as Face Detection / Recognition / Parsing dlib and MTCNN. WHO THIS BOOK IS FOR This book is designed specially for computer vision users, machine learning engineers, image processing experts who are looking for solving modern image processing/computer vision challenges. TABLE OF CONTENTS 1. Chapter 1: Basic Image & Video Processing 2. Chapter 2: More Image Transformation and Manipulation 3. Chapter 3: Sampling, Convolution and Discrete Fourier Transform 4. Chapter 4: Discrete Cosine / Wavelet Transform and Deconvolution 5. Chapter 5: Image Enhancement 6. Chapter 6: More Image Enhancement 7. Chapter 7: Facel Image Processing