Image Processing

Part Overview

Every system in this book, from an edge detector to a text-to-image model, begins with the same humble object: a rectangular grid of numbers. Part I is the story of that grid. Before a network can recognize a pedestrian or a diffusion model can paint one, someone has to load the image correctly, untangle its color channels, balance its contrast, suppress its noise, and put its geometry right. That craft is image processing, and it predates deep learning by half a century. The nine chapters in this part teach it the way a working engineer needs it: with NumPy arrays on screen, OpenCV and scikit-image at hand, and a clear account of the signal-processing ideas underneath each function call.

The chapters build in deliberate order. Chapter 0 sets up the workbench: images as NumPy arrays, the Python imaging ecosystem, and the conventions (BGR versus RGB, uint8 versus float) that cause most beginner bugs. Chapter 1 steps back to ask where the array comes from, following light from photons through sensors, sampling, and quantization to color spaces and compressed files. Chapter 2 then makes the first changes to an image, one pixel at a time: brightness, gamma, histograms, and thresholding. Chapter 3 widens the view from a single pixel to its neighborhood and introduces convolution, the single most important operation in this book. Chapter 4 changes the coordinate system entirely, viewing images as sums of waves; the frequency domain explains aliasing, JPEG compression, and image pyramids in one stroke. Chapter 5 moves the pixels themselves, with the transformation matrices and interpolation methods behind rotation, rectification, and registration. Chapter 6 reduces images to binary masks and shows how a small algebra of erosions and dilations carries a surprising share of industrial vision. Chapter 7 assembles everything into restoration: undoing noise, blur, missing pixels, and limited dynamic range. Chapter 8 closes the part with a consolidated reference for its libraries, performance tooling, and datasets.

A recurring thread runs through this part and far beyond it: ideas introduced here classically return later, learned. The convolution kernel of Chapter 3 becomes the trainable CNN layer of Part III. The denoising problem of Chapter 7 becomes, almost verbatim, the training objective of diffusion models in Part IV, and inpainting returns there as generative editing. Part I is therefore not a warm-up to skim; it is the vocabulary the rest of the book is written in. Read it with an interpreter open, run the pipelines, and the later parts will feel like extensions of tools you already own.