Next: Analyzing Signals and Images
Applying Wavelet Methods
Wavelet methods provide functions and an app for analyzing, encoding, compressing, reconstructing, and modeling signals and images. They are useful in capturing, identifying, and analyzing local, multiscale, and nonstationary processes, enabling you to explore aspects of data that other analysis techniques miss, such as trends, breakdown points, discontinuities in higher derivatives, and self-similarity.
Wavelet decomposition using wavelet packet analysis.
Wavelet Toolbox supports a full suite of wavelet analysis and synthesis operations. You can use it to:
- Enhance edge detection in image processing
- Achieve high rates of signal or image compression with virtually no loss of significant data
- Restore noisy signals and degraded images
- Discover trends in noisy or faulty data
- Study the fractal properties of signals and images
- Extract information-rich features for use in classification and pattern recognition applications
- Perform multivariate denoising of signals with multiscale principal component analysis
Image from the U.S. Federal Bureau of Investigation fingerprint database. The automatic thresholding feature of Wavelet Toolbox produces a compressed image with about 72% zeros and 98% of the original signal.