Software and Images accompanying this book. 2nd Edition 2016.

2nd Edition, 2016. This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. The book weds theory and practice in examining applications in areas such as astronomy, biology, physics, digital media, and forensics.

  • Pyramidal Wavelet Transform on the Sphere

    The global relief data are ETOPO5 land and sea-floor elevation data. The isotropic pyramidal wavelet transform preserves detail, and is efficient in storage and computation. Chapter 10 discusses many aspects of multiscale geometric analysis on the sphere.

  • Filtering Biomedical Microscope Images

    The image (upper left) is of fluorescent tubulin filaments. Upper right then is a noisy version, with Poisson noise. The lower images are filtered, as discussed in Chapter 6. This noise filtering is based on the curvelet transform for preservation of faint features.

  • Signal Recovery from Compressed Sampling

    Compressed sensing simultaneously samples and compresses the signal. The image of Piet Mondrian's painting is an example used in Chapter 11 to show how compressed sensing handles acquisition and transmission of large amounts of image data.