|
Curvelet
Curvelets are a non-adaptive technique for multi-scale object representation. Being an extension of the wavelet concept, they are becoming popular in similar fields, namely in image processing and scientific computing. Wavelets generalize the Fourier transform by using a basis that represents both location and spatial frequency. For 2D or 3D signals, directional wavelet transforms go further, by using basis functions that are also localized in ''orientation''. A curvelet transform differs from other directional wavelet transforms in that the degree of localisation in orientation varies with scale. In particular, fine-scale basis functions are long ridges; the shape of the basis functions at scale ''j'' is 2^ by 2^ so the fine-scale bases are skinny ridges with a precisely determined orientation. Curvelets are an appropriate basis for representing images (or other functions) which are smooth apart from singularities along smooth curves, ''where the curves have bounded curvature' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Contourlet
Contourlets form a multiresolution directional tight frame designed to efficiently approximate images made of smooth regions separated by smooth boundaries. The contourlet transform has a fast implementation based on a Laplacian pyramid decomposition followed by directional filterbanks applied on each bandpass subband. Contourlet transform Introduction and motivation In the field of geometrical image transforms, there are many 1-D transforms designed for detecting or capturing the geometry of image information, such as the Fourier and wavelet transform. However, the ability of 1-D transform processing of the intrinsic geometrical structures, such as smoothness of curves, is limited in one direction, then more powerful representations are required in higher dimensions. The contourlet transform which was proposed by Do and Vetterli in 2002, is a new two-dimensional transform method for image representations. The contourlet transform has properties of multiresolution, localization, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Shearlet
In applied mathematical analysis, shearlets are a multiscale framework which allows efficient encoding of anisotropic features in multivariate problem classes. Originally, shearlets were introduced in 2006 for the analysis and sparse approximation of functions f \in L^2(\R^2). They are a natural extension of wavelets, to accommodate the fact that multivariate functions are typically governed by anisotropic features such as edges in images, since wavelets, as isotropic objects, are not capable of capturing such phenomena. Shearlets are constructed by parabolic scaling, shearing, and translation applied to a few generating functions. At fine scales, they are essentially supported within skinny and directional ridges following the parabolic scaling law, which reads ''length² ≈ width''. Similar to wavelets, shearlets arise from the affine group and allow a unified treatment of the continuum and digital situation leading to faithful implementations. Although they do not constitute ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Wavelet
A wavelet is a wave-like oscillation with an amplitude that begins at zero, increases or decreases, and then returns to zero one or more times. Wavelets are termed a "brief oscillation". A taxonomy of wavelets has been established, based on the number and direction of its pulses. Wavelets are imbued with specific properties that make them useful for signal processing. For example, a wavelet could be created to have a frequency of Middle C and a short duration of roughly one tenth of a second. If this wavelet were to be convolved with a signal created from the recording of a melody, then the resulting signal would be useful for determining when the Middle C note appeared in the song. Mathematically, a wavelet correlates with a signal if a portion of the signal is similar. Correlation is at the core of many practical wavelet applications. As a mathematical tool, wavelets can be used to extract information from many different kinds of data, including but not limited to au ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Emmanuel Candès
Emmanuel Jean Candès (born 27 April 1970) is a French statistician. He is a professor of statistics and electrical engineering (by courtesy) at Stanford University, where he is also the Barnum-Simons Chair in Mathematics and Statistics. Candès is a 2017 MacArthur Fellow. Academic biography Candès earned a MSc from the École Polytechnique in 1993.Speaker bio from NIPS 2008. He did his postgraduate studies at , where he earned a PhD in statistics in 1998 under the supervision of |
Adaptive-additive Algorithm
In the studies of Fourier optics, sound synthesis, stellar interferometry, optical tweezers, and diffractive optical elements (DOEs) it is often important to know the spatial frequency phase of an observed wave source. In order to reconstruct this phase the Adaptive-Additive Algorithm (or AA algorithm), which derives from a group of adaptive (input-output) algorithms, can be used. The AA algorithm is an iterative algorithm that utilizes the Fourier Transform to calculate an unknown part of a propagating wave, normally the spatial frequency phase (k space). This can be done when given the phase’s known counterparts, usually an observed amplitude (position space) and an assumed starting amplitude (k space). To find the correct phase the algorithm uses error conversion, or the error between the desired and the theoretical intensities. The algorithm History The adaptive-additive algorithm was originally created to reconstruct the spatial frequency phase of light intensity in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Image Processing
An image is a visual representation of something. It can be two-dimensional, three-dimensional, or somehow otherwise feed into the visual system to convey information. An image can be an artifact, such as a photograph or other two-dimensional picture, that resembles a subject. In the context of signal processing, an image is a distributed amplitude of color(s). In optics, the term “image” may refer specifically to a 2D image. An image does not have to use the entire visual system to be a visual representation. A popular example of this is of a greyscale image, which uses the visual system's sensitivity to brightness across all wavelengths, without taking into account different colors. A black and white visual representation of something is still an image, even though it does not make full use of the visual system's capabilities. Images are typically still, but in some cases can be moving or animated. Characteristics Images may be two or three-dimensional, such as a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gerlind Plonka
Gerlind Plonka-Hoch is a German applied mathematician specializing in signal processing and image processing, and known for her work on refinable functions and curvelets. She is a professor at the University of Göttingen, in the Institute for Numerical and Applied Mathematics. Plonka earned her Ph.D. from the University of Rostock in 1993. Her dissertation, ''Periodische Lagrange- und Hermite-Spline-Interpolation'', concerned polynomial interpolation using Lagrange polynomials and Hermite splines, and was supervised by Manfred Tasche. She was the Emmy Noether Lecturer of the German Mathematical Society The German Mathematical Society (german: Deutsche Mathematiker-Vereinigung, DMV) is the main professional society of German mathematicians and represents German mathematics within the European Mathematical Society (EMS) and the International Math ... in 2016. Book * with Reviews of ''Numerical Fourier Analysis'': Raffaele D'Ambrosio, ; Adhemar Bultheel, References Externa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Scale Space
Scale-space theory is a framework for multi-scale signal representation developed by the computer vision, image processing and signal processing communities with complementary motivations from physics and biological vision. It is a formal theory for handling image structures at different scales, by representing an image as a one-parameter family of smoothed images, the scale-space representation, parametrized by the size of the smoothing kernel used for suppressing fine-scale structures.Ijima, T. "Basic theory on normalization of pattern (in case of typical one-dimensional pattern)". Bull. Electrotech. Lab. 26, 368– 388, 1962. (in Japanese) The parameter t in this family is referred to as the ''scale parameter'', with the interpretation that image structures of spatial size smaller than about \sqrt have largely been smoothed away in the scale-space level at scale t. The main type of scale space is the ''linear (Gaussian) scale space'', which has wide applicability as well as ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Noiselet
Noiselets are functions which gives the worst case behavior for the Haar wavelet packet analysis. In other words, noiselets are totally incompressible by the Haar wavelet packet analysis.R. Coifman, F. Geshwind, and Y. Meyer, Noiselets, Applied and Computational Harmonic Analysis, 10 (2001), pp. 27–44. . Like the canonical and Fourier bases, which have an incoherent property, noiselets are perfectly incoherent with the Haar basis. In addition, they have a fast algorithm for implementation, making them useful as a sampling basis for signals that are sparse in the Haar domain. Definition The mother bases function \chi(x) is defined as: \chi(x)= \begin 1 & x\in[0,1) \\ 0 & \text \end The family of noislets is constructed recursively as follows: \begin f_1(x) &= \chi(x)\\ f_(x) &= (1-i)f_n(2x)+(1+i)f_n(2x-1)\\ f_(x) &= (1+i)f_n(2x)+(1-i)f_n(2x-1) \end Property of fn * \ is an orthogonal basis for V_N , where V_N is the space of all possible approximations at the re ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Chirplet Transform
In signal processing, the chirplet transform is an inner product of an input signal with a family of analysis primitives called chirplets.S. Mann and S. Haykin,The Chirplet transform: A generalization of Gabor's logon transform, ''Proc. Vision Interface 1991'', 205–212 (3–7 June 1991).D. Mihovilovic and R. N. Bracewell, "Adaptive chirplet representation of signals in the time–frequency plane," ''Electronics Letters'' 27 (13), 1159–1161 (20 June 1991). Similar to the wavelet transform, chirplets are usually generated from (or can be expressed as being from) a single ''mother chirplet'' (analogous to the so-called '' mother wavelet'' of wavelet theory). Definitions The term ''chirplet transform'' was coined by Steve Mann, as the title of the first published paper on chirplets. The term ''chirplet'' itself (apart from chirplet transform) was also used by Steve Mann, Domingo Mihovilovic, and Ronald Bracewell to describe a windowed portion of a chirp function. In ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fresnel Transform
In optics, the Fresnel diffraction equation for near-field diffraction is an approximation of the Kirchhoff–Fresnel diffraction that can be applied to the propagation of waves in the near field. It is used to calculate the diffraction pattern created by waves passing through an aperture or around an object, when viewed from relatively close to the object. In contrast the diffraction pattern in the far field region is given by the Fraunhofer diffraction equation. The near field can be specified by the Fresnel number, , of the optical arrangement. When F \gg 1 the diffracted wave is considered to be in the near field. However, the validity of the Fresnel diffraction integral is deduced by the approximations derived below. Specifically, the phase terms of third order and higher must be negligible, a condition that may be written as \frac \ll 1, where \theta is the maximal angle described by \theta \approx a/L, and the same as in the definition of the Fresnel number. The m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bandelet
Bandelets are an orthonormal basis that is adapted to geometric boundaries. Bandelets can be interpreted as a warped wavelet basis. The motivation behind bandelets is to perform a transform on functions defined as smooth functions on smoothly bounded domains. As bandelet construction utilizes wavelets, many of the results follow. Similar approaches to take account of geometric structure were taken for contourlets and curvelets. See also *Wavelet *Multiresolution analysis *Scale space Scale-space theory is a framework for multi-scale signal representation developed by the computer vision, image processing and signal processing communities with complementary motivations from physics and biological vision. It is a formal theor ... References * * External links Bandelet toolboxon MatLab Central Wavelets {{computer-science-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
