🧠 Digital Image Processing — Interactive Notebooks

This repository contains an educational journey through Digital Image Processing (DIP) using Jupyter Notebooks, blending:

• Clear theoretical explanations
• Mathematical foundations
• Visual intuition
• Real-world examples
• Python implementations

The content is inspired by:

• The course “Processamento Digital de Imagens” taught by Prof. Dr. Jesuliana N. Ulysses (UFSJ)
• The book “A Computational Introduction to Digital Image Processing” — Alasdair McAndrew

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🎯 Objectives

This project aims to be a self-contained learning path for students and practitioners, offering:

1. Interactive notebooks that build concepts step-by-step
2. Hands-on exercises to reinforce learning
3. Real images & experiments to visualize concepts
4. Clean modular structure, mirroring a university-level DIP course
5. A foundation for future computer vision topics (filters, segmentation, ML, CV tasks)

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🚀 How to Start Learning

Start in the first module:

👉 01-introduction/01-introduction.ipynb

Then follow notebooks in numeric order. Each module contains:

• Core theory notebook(s)
• Python experiments
• Exercises
• Practice activities

Recommended Flow

1. Concept notebook — understand theory
2. Code demo notebook — experiment visually
3. Exercises — confirm understanding
4. Practice notebooks — explore real world challenges

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📂 Repository Structure

image-processing/
│
├── 01-introduction/
│   ├── notebooks/
│   │   ├── 01-introduction.ipynb
│   │   ├── 02-digital-images.ipynb
│   │   ├── 03-sampling-quantization.ipynb
│   │   ├── 04-python-environment.ipynb
│   │   └── 05-first-operations.ipynb
│   │
│   ├── exercises/
│   │   ├── Exercise01.ipynb
│   │
│   ├── practices/
│   │   ├── Practice01.ipynb
│   │
│   ├── images/
│   │   ├── source/
│   │   │   ├── lena_gray.png
│   │   │   ├── cameraman.png
│   │   │   └── ...
│   └── └── outputs/
│
├── 02-fundamentals/
│   ├── notebooks/
│   ├── exercises/
│   ├── practices/
│   ├── images/
│   │   ├── source/
│   └── └── outputs/
│
│   03-.../
│   04-.../
│
└── README.md

🔁 Each folder = one learning module 📓 Each module = theory + experiments + exercises + practice

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🧰 Tools & Libraries

We use the standard Python image-science stack:

• Python 3
• NumPy — matrix & pixel operations
• Matplotlib — plotting & visualization
• OpenCV (cv2) — image processing toolbox
• scikit-image (skimage) — scientific image algorithms
• Jupyter Notebook — interactive learning environment

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🧑‍🏫 Learning Style

All notebooks are written for clarity and intuition, using:

• Step-by-step explanations
• Visual reasoning
• Code comments & inline notes
• Side-by-side original vs processed results
• Notebook questions & reflection prompts

Where applicable, we also include:

• Pixel grids
• Histograms
• Annotated diagrams
• Progressive examples (simple → real images)

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🪶 References

Course materials by Prof. Dr. Jesuliana N. Ulysses (UFSJ) A Computational Introduction to Digital Image Processing — Alasdair McAndrew

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📬 Author

Caio Fromm 📎 GitHub: https://github.com/fromcaio