This is a review paper on the steerable pyramid, a linear multiscale, multiorientation image decomposition that finds use in the preprocessing stage of image processing and computer vision applications. Orthogonal separable wavelet transforms, once popular for the same tasks, give rise to heavily aliased representations that do not represent diagonal orientations well. To overcome these limitat...

Image fusion combines several images of the same scene into a fused image, which contains all important information. Multiscale transform and sparse representation can solve this problem effectively. However, due to the limited number of dictionary atoms, it is difficult to provide an accurate description for image details in the sparse representation–based image fusion method, and it needs a g...

The multi-resolution watermarking method for digital images proposed in this work. The multiscale ridgelet coefficients of low and high frequency bands of the watermark is embedded to the most significant coefficients at low and high frequency bands of the multiscale ridgelet of an host image, respectively. A multi-resolution nature of multiscale ridgelet transform is exploiting in the process ...

Many works have been achieved for analyzing images with a multiscale approach. In this paper, an intrinsic and nonlinear multiscale image decomposition is proposed, based on partial differential equations (PDEs) and the image frequency contents. Our model is inspired from the 2D empirical mode decomposition (EMD) for which a theoretical study is quite nonexistent, mainly because the algorithm i...

purpose: to present a new reliable and accurate iris recognition method applicable in identification systems. methods: the system was implemented and tested on 876 standard iris images (daugman iris images database) from 876 persons of different nationalities via image processing techniques. results: false accept reject (far) and false reject reject (frr) were smaller in our suggested method as...

The Gauss Center research on multiscale computational methods is reported, emphasizing main ideas and interrelations between various elds, and listing the relevant bibliography. The reported areas include: top-eeciency multigrid methods in uid dynamics; atmospheric ows and data assimilation; feedback optimal control; PDE solvers on unbounded domains; wave/ray methods for highly indeenite equati...

Denoising and image enhancement pre-processing techniques are fundamental for segmentation and classification purposes in a wide range of applications. Particularly, in the field of remote sensing, where synthetic aperture radar, (SAR) images are characterized by the intrinsic multiplicative noise, so-called speckle which affects negatively image analysis techniques, such as automatic target re...

We develop a new class of non-Gaussian multiscale stochastic processes defined by random cascades on trees of multiresolution coefficients. These cascades reproduce a semiparametric class of random variables known as Gaussian scale mixtures, members of which include many of the best known, heavy-tailed distributions. This class of cascade models is rich enough to accurately capture the remarkab...

In this paper we briefly describe advancements in two broad areas of morphological image analysis. Part I deals with differential morphology and curve evolution. The partial differential equations (PDEs) that model basic morphological operations are first presented. The resulting dilation PDE, numerically implemented by curve evolution algorithms, improves the accuracy of morphological multisca...

A multiscale image registration technique is presented for the registration of medical images that contain significant levels of noise. An overview of the medical image registration problem is presented, and various registration techniques are discussed. Experiments using mean squares, normalized correlation, and mutual information optimal linear registration are presented that determine the no...