Digital Image Processing: An Algorithmic Introduction Using Java (Texts in Computer Science)
Written as an introduction for undergraduate students, this textbook covers the most important methods in digital image processing. Formal and mathematical aspects are discussed at a fundamental level and various practical examples and exercises supplement the text. The book uses the image processing environment ImageJ, freely distributed by the National Institute of Health. A comprehensive website supports the book, and contains full source code for all examples in the book, a question and answer forum, slides for instructors, etc. Digital Image Processing in Java is the definitive textbook for computer science students studying image processing and digital processing.
Why Read This Book
You will get a practical, algorithm-first introduction to digital image processing with complete, ready-to-run Java examples (ImageJ) so you can prototype and test ideas quickly. The book emphasizes clear explanations of core transforms, spatial and frequency-domain filtering, segmentation, and morphology so you can move from theory to working code.
Who Will Benefit
Undergraduate students, engineers, or developers with basic math/programming skills who want hands‑on exposure to image-processing algorithms and working Java/ImageJ code examples.
Level: Intermediate — Prerequisites: Basic calculus and linear algebra, familiarity with programming (Java preferred) and basic signal/image concepts; no advanced background required.
Key Takeaways
- Implement basic image I/O and preprocessing pipelines in Java using ImageJ
- Apply spatial-domain filters and design convolution kernels for denoising and sharpening
- Use 2D Fourier transforms to perform frequency-domain filtering and analyze image spectra
- Design and implement edge detectors and gradient-based feature extraction methods
- Perform image segmentation and apply morphological operations for shape processing
- Prototype, test, and visualize image-processing algorithms using supplied source code
Topics Covered
- 1. Introduction to Digital Images and Image I/O (ImageJ)
- 2. Intensity Transforms and Histograms
- 3. Sampling, Quantization, and Aliasing
- 4. Linear Filtering and Convolution
- 5. Correlation and Template Matching
- 6. Frequency Domain: 2D DFT and FFT
- 7. Frequency-Domain Filtering and Image Restoration
- 8. Noise Models and Denoising Techniques
- 9. Edge Detection and Gradient Operators
- 10. Segmentation Techniques (Thresholding, Region Growing)
- 11. Morphological Image Processing
- 12. Feature Extraction and Image Descriptors
- 13. Color Image Processing
- 14. Practical Projects, Examples and Exercises (ImageJ code)
Languages, Platforms & Tools
How It Compares
More hands-on and Java/ImageJ–centric than Gonzalez & Woods' classic textbook (which is MATLAB-focused and broader in theory); more algorithmic and code-driven than Szeliski's vision text, which emphasizes higher-level vision topics.
