Signal and Image Processing with Neural Networks: A C++ Sourcebook
Demonstrates how neural networks can be used to aid in the solution of digital signal processing (DSP) or imaging problems. A large section is devoted to the design and training of complex-domain multiple-layer feedforward networks (MLFNs)—all essential equations are presented and justified. Reviews the most popular signal- and image-processing algorithms, emphasizing those that are particularly suitable for union to complex-domain neural networks. Features a wide variety of problems for which complex-domain networks significantly outperform their real-domain counterparts. The accompanying disk includes complete source code for algorithms discussed with full source for program examples.
Why Read This Book
You will get a pragmatic, example-driven view of how neural networks were applied to signal and image processing problems before the deep‑learning era, including detailed equations and training recipes for complex‑domain multi‑layer feedforward networks. The book ships with full C++ source so you can inspect and adapt working implementations of denoising, classification, spectral tasks and other DSP routines.
Who Will Benefit
Practicing DSP engineers or researchers with some exposure to neural networks who want concrete algorithms and C++ implementations for applying NNs to signals and images.
Level: Intermediate — Prerequisites: Basic DSP/signals and systems, linear algebra and complex numbers, and familiarity with C++ programming and general machine learning concepts (e.g., supervised training, backpropagation).
Key Takeaways
- Implement C++ prototypes of multilayer feedforward neural networks for signal and image tasks.
- Design and train complex‑domain neural networks and understand when complex representations outperform real ones.
- Apply neural networks to classic DSP problems such as denoising, classification, and spectral feature extraction.
- Compare neural methods with traditional DSP algorithms and assess performance tradeoffs.
- Adapt training algorithms (backpropagation variants) to the numerical and stability issues of DSP data.
Topics Covered
- 1. Introduction: Neural Networks in Signal and Image Processing
- 2. Fundamentals of Neurons and Multilayer Feedforward Networks
- 3. Training Algorithms and Backpropagation Variants
- 4. Complex‑Valued Neural Networks: Theory and Motivation
- 5. Design and Stability of Complex MLFNs
- 6. Neural Network Approaches to Spectral and Filtering Problems
- 7. Image Processing Applications: Denoising, Restoration, and Segmentation
- 8. Classification and Pattern Recognition for Signals and Images
- 9. Practical Implementation: C++ Source, Data Structures and I/O
- 10. Performance Comparisons with Classical DSP Methods
- 11. Case Studies and Example Problems
- 12. Appendices: Code Listings and Numerical Recipes
Languages, Platforms & Tools
How It Compares
Less theory‑heavy than Haykin's canonical texts and more applied than academic proceedings on neural networks for signal processing — it emphasizes complex‑domain nets and ships with working C++ code.
