The Image Processing Handbook, Fifth Edition
Now in its fifth edition, John C. Russ’s monumental image processing reference is an even more complete, modern, and hands-on tool than ever before. The Image Processing Handbook, Fifth Edition is fully updated and expanded to reflect the latest developments in the field. Written by an expert with unequalled experience and authority, it offers clear guidance on how to create, select, and use the most appropriate algorithms for a specific application.
What’s new in the Fifth Edition?
· A new chapter on the human visual process that explains which visual cues elicit a response from the viewer
· Description of the latest hardware and software for image acquisition and printing, reflecting the proliferation of the digital camera
· New material on multichannel images, including a major section on principal components analysis
· Expanded sections on deconvolution, extended dynamic range images, and image enlargement and interpolation
· More than 600 new and revised figures and illustrations for a total of more than 2000 illustrations
· 20% more references to the most up-to-date literature
Written in a relaxed and reader-friendly style, The Image Processing Handbook, Fifth Edition guides you through the myriad tools available for image processing and helps you understand how to select and apply each one.
Why Read This Book
You should read this book if you want a practical, wide-ranging reference that connects image theory to real-world implementation — from acquisition through enhancement, restoration, segmentation and multiresolution methods. It gives you actionable guidance and worked examples so you can pick appropriate algorithms and apply them to imaging problems quickly.
Who Will Benefit
Engineers and applied researchers working on image analysis, computer vision, or signal-processing-based imaging tasks who need a hands-on reference for algorithms and implementation choices.
Level: Intermediate — Prerequisites: Familiarity with basic calculus, linear algebra, and fundamental DSP concepts (sampling, convolution, Fourier transforms); experience with a scripting language (e.g., MATLAB or Python) is helpful.
Key Takeaways
- Implement common spatial-domain and frequency-domain image enhancement and filtering techniques.
- Apply FFT-based and convolutional methods for image restoration and noise reduction.
- Design and evaluate segmentation, edge-detection, and morphological processing pipelines.
- Use multiresolution/wavelet tools for compression, denoising, and feature extraction.
- Interpret image acquisition and display issues, including sampling, quantization, color, and perceptual considerations.
- Choose appropriate algorithms and quality metrics for real-world imaging tasks and workflows.
Topics Covered
- Introduction and history of digital image processing
- Human visual process and perceptual considerations
- Image acquisition, scanners, and digital cameras
- Image sampling, quantization, and color models
- Point operations, histograms, and contrast enhancement
- Spatial-domain filtering and morphological operations
- Frequency-domain analysis, FFTs, and spectral filtering
- Image restoration and deblurring
- Segmentation, edge detection, and feature extraction
- Texture analysis and pattern description
- Wavelets and multiresolution image processing
- Compression, printing, and output devices
- Practical implementation notes, hardware, and software
Languages, Platforms & Tools
How It Compares
More applied and implementation-focused than Gonzalez & Woods' Digital Image Processing, serving as a practical handbook rather than a classroom-first theoretical textbook.
