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Dr. Joshua Stough, 202-

An interactive Jupyter book for digital image processing.

A collage of figures from throughout the book

Preface

Imaging is everywhere! In this text, we will cover broadly the acquisition, processing, and analysis of digital images, covering topics ranging from the human visual system, to image and video compression algorithms, to pattern recognition and machine learning within the context of automatic image understanding. Best of all, for the sake of access, immediacy, and usability, all content and code examples are in the form of interactive Jupyterlab notebooks, including integrated activities!

Written with extensive assistance of Claude in drafting narrative and copyediting, and backend book production, based on the draft notebook collection at joshuastough/imageprocessing.

A note on AI

With the advent of IDE-integrated LLM copilots, any of the integrated activities or playpen notebooks are trivial to complete. That is, your completion of them reflect almost nothing about your understanding of the material.

If you’re in class using this textbook resource, you can be expected to have to explain your work in non-augmented coding interviews throughout the course, which will comprise much of your grade. These integrated activities are to help guide your learning, not add to your instructor’s menial labor.

Using this Book

Every section is a Jupyter notebook you can run, change, and break. You can work through the book in one of three ways:

  • Locally (recommended): clone your private fork, run uv sync, and open the notebooks in VS Code or JupyterLab with the .venv kernel. See the README for details, including where to put the datasets a few chapters need.

  • In Colab: run the Colab setup notebook (there’s an Open in Colab badge in the README). It copies the book into your Google Drive and gives you a short setup cell for each notebook you open. No install needed, and Colab offers a free GPU.

  • Just reading: the notebooks render on GitHub, though the interactive figures won’t respond there.

Notebooks marked 🔨 exercise are activities, with empty cells for you to complete. demo notebooks are short, focused examples. extra notebooks are optional asides.

Within the notebooks, two kinds of sections ask you to do the work yourself:

  • 🔨 Your Turn sections pause a notebook to give you a specific task, with some guidance on how to approach it, followed by an empty code cell for your work. The rest of the notebook never depends on your answer, so you can come back to them.

  • 🧠 Further Efforts close many notebooks with a list of more open-ended problems, the counterpart of a textbook’s end-of-chapter problem set. Each comes with a hint about where to start.

 

Topics

1. Numpy And Visualization

Numpy Tutorial Matplotlib Tutorial Interactive Visualization Probability Distributions, Gaussian and Uniform extra viewing video

2. Sensing and Acquisition, Sampling and Quantization

Sampling and Spatial Resolution Color Resolution or Quantization demo heightmap 🔨 exercise playing with images extra spatial resolution gif maker

3. Color

Brightness, Hue, Saturation, Chromaticity HSV YCbCr L*a*b* and CIE Illuminants 🔨 exercise playing with color

4. Contrast and Image Enhancement

Transfer Functions Histogram Equalization demo gray flower transforms 🔨 exercise playing with contrast enhancement extra bit-slicing and steganography

5. Information Theory, Entropy, and Huffman Coding

Measuring Entropy, Huffman Coding and Image Compression Predictive Coding and PNG Compression Color Clustering extra picking colors

Communication, Compression, and Perception (coming soon)

6. Coordinate Systems

Coordinate Operations Geometric Transforms

7. Spatial Filtering, Smoothing and Sharpening

Spatial Operations Convolution and Kernels, as 3D Height Maps Smoothing: Low-pass Filtering Sharpening: High-pass Filtering Generic and Non-linear Filtering Gradient Maps and Edge Detection

8. Basis Sets, Transform Coding, Wavelet Compression

Block Transforms Spatial Coherence and Change of Basis Viewing Basis Blocks Haar Wavelets demo basis decomposition of a 64-pixel image 🔨 exercise Haar compression 🔨 exercise DCT introduction 🔨 exercise JPEG compression Comparing Reconstruction Efficiency extra glyph images

9. Image Analysis, Machine Learning

Points, Edges, Regions

Otsu’s Method

Canny Edge Detection: see Gradient Maps and Edge Detection, under Spatial Filtering

Superpixel Clustering

demo k-means in 2D demo k-means on an image SLIC Superpixels

Machine Learning Basics: Features, Loss, Optimization

MNIST: exploring the data 🔨 exercise MNIST by template correlation MNIST: a linear classifier MNIST: a convolutional network

Face Recognition

Exploring the Yale faces A convolutional network on Yale faces

10. Miscellanea

Principal Component Analysis

PCA in 2D PCA on the Iris data PCA on MNIST Face Space Component Spans Reconstruction

Karhunen-Loève Transform and Compression: see Comparing Reconstruction Efficiency, under Basis Sets

Tomography and the Radon Transform

Radon forward example demo Radon dot animation demo draw your own Radon 🔨 exercise playing with the Radon transform

Fourier and Frequency Encoding

The Discrete Fourier Transform Fourier Transform Blurring and Sharpening in the Frequency Domain

CUDA and Graphics Processing Units

Comparing CPU and GPU with torch

Numpy And Visualization
Numpy Tutorial