Digital Image Processing Using MATLAB, 2nd ed. by Rafael C. Gonzalez (2009-05-03)
Digital Image Processing Using MATLAB is the first book to offer a balanced treatment of image processing fundamentals and the software principles used in their implementation. The book integrates material from the leading text, Digital Image Processing by Gonzalez and Woods, and the Image Processing Toolbox from The MathWorks, Inc., a leader in scientific computing. The Image Processing Toolbox provides a stable, well-supported software environment for addressing a broad range of applications in digital image processing. A unique feature of the book is its emphasis on showing how to enhance those tools by developing new code. This is important in image processing, an area that normally requires extensive experimental work in order to arrive at acceptable application solutions. Some Highlights: (1) This new edition is an extensive upgrade of the book. (2) Over 120 new MATLAB image processing functions are developed, a 40 % increase over existing functions in the Image Processing Toolbox. (3) Algorithms and MATLAB functions in the mainstream of digital image processing are discussed and implemented, including: Intensity transformations; spatial filtering; fuzzy image processing; filtering in the frequency domain; image restoration and reconstruction; geometric transformations and image registration; color image processing; wavelets; image and video compression; morphology; image segmentation; image representation and description; and object recognition. (4) In addition to a major revision of the topics from the first edition, features in this edition include new coverage of: The Radon transform; image processing functions based on function-generating functions (function factories); geometric transformations; image registration; color profiles and device-independent color conversions; functions for video compression; adaptive thresholding algorithms; new image features, including minimum-perimeter polygons and local (corner) features.
Why Read This Book
You will get a hands‑on bridge between image processing theory and working code: the book shows how classic algorithms from Gonzalez & Woods are implemented, tested, and extended in MATLAB. If you need to prototype, experiment, or justify algorithm choices with reproducible MATLAB examples, this book makes that fast and practical.
Who Will Benefit
Engineers and graduate students who know basic signals/math and want practical MATLAB implementations of image processing algorithms for prototyping, research, or product development.
Level: Intermediate — Prerequisites: Basic linear algebra and calculus, introductory signals/systems or DSP concepts, and familiarity with MATLAB (scripts, matrices, plotting).
Key Takeaways
- Implement common image processing algorithms in MATLAB using both hand-coded routines and Image Processing Toolbox functions.
- Apply spatial and frequency‑domain filters for smoothing, sharpening, and restoration of images.
- Perform segmentation, edge detection, morphological processing, and feature extraction for analysis and recognition tasks.
- Use transforms (FFT, DCT, wavelets) and multiresolution techniques for compression, denoising, and analysis.
- Evaluate algorithm performance with image quality metrics and design experiments to tune parameters.
- Adapt and extend toolbox routines by writing custom MATLAB code for application‑specific solutions.
Topics Covered
- 1. Introduction and MATLAB Primer
- 2. Fundamentals of Digital Images
- 3. Intensity Transformations and Spatial Filtering
- 4. Frequency Domain Processing for Images
- 5. Image Enhancement and Restoration
- 6. Color Image Processing
- 7. Wavelets and Multiresolution Processing
- 8. Morphological Image Processing
- 9. Image Segmentation
- 10. Feature Extraction and Representation
- 11. Image Compression
- 12. Practical MATLAB Examples and Toolbox Use
- Appendices: File I/O, Toolboxes, and Sample Projects
Languages, Platforms & Tools
How It Compares
This book complements Gonzalez & Woods' Digital Image Processing by providing MATLAB code and experiments (more applied), and is more MATLAB‑centric than Sonka et al.'s Image Processing, Analysis, and Machine Vision which is broader in vision/vision‑system design.
