Sampling Theorems of Wavelet Subspaces with M Band in Higher Dimensions

2013 ◽  
Vol 709 ◽  
pp. 563-566
Author(s):  
Jin Zhou Li ◽  
Xin Cheng Zhang
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Symmetry Property ◽  
Scaling Function ◽  
Higher Dimensions ◽  
Sampling Theorem ◽  
Scaling Functions ◽  
Sampling Theorems ◽  
Highpass Filter ◽  
Wavelet Subspaces

Sampling theorem has a key role in signal processing and image processing. In this paper, the scaling functions with cardinal property are discussed in the dimensions and their symmetry property is classified. Therefore, sampling theorems of wavelet subspaces are obtained. Then, the cardinal orthogonal scaling function with cardinal property is characterized in the dimensions and an equation between the highpass filter coefficients and wavelet samples are got. The existing results are generalized to the case of M band.

Four Band Cardinal Orthogonal Scaling Functions and Wavelets in Higher Dimensions

2013 ◽  
Vol 275-277 ◽  
pp. 2523-2526
Author(s):  
Xin Cheng Zhang
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Symmetry Property ◽  
Scaling Function ◽  
Digital Communications ◽  
Higher Dimensions ◽  
Sampling Theorem ◽  
Scaling Functions ◽  
Highpass Filter

Sampling theorem plays an important role in the engineering such as signal processing, image processing, digital communications, and so on. In this paper, the symmetry property of cardinal orthogonal scaling function is discussed. Then, a 4-band cardinal orthogonal scaling function from the relation between the highpass filter coefficients and wavelet is provided. Thus, sampling theorem in the wavelet subspace is obtained.

Classifying Cardinal Orthogonal Scaling Function with Dilation Factor 3

2011 ◽  
Vol 282-283 ◽  
pp. 437-439
Author(s):  
Lan Feng Wang ◽  
Kai Jun Sun ◽  
Jing Ben Yin
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Symmetry Property ◽  
Scaling Function ◽  
Sampling Theorem ◽  
Filter Coefficient ◽  
Highpass Filter ◽  
Dilation Factor

Sampling theorem plays an important role in many fields such as signal processing and image processing. In this paper, the cardinal orthogonal scaling function with dilation factor 3 is classified by the highpass filter coefficient, thus, the sampling theorem in the wavelet subspace is obtained. Then, the symmetry property of cardinal orthogonal scaling function is discussed.

The M-Band Symmetric Orthogonal Scaling Function in Higher Dimensions

2013 ◽  
Vol 482 ◽  
pp. 322-325
Author(s):  
Fang Jun Zhang
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Scaling Function ◽  
Higher Dimensions ◽  
One Dimension ◽  
Wavelet Theory

Wavelet theory has a key role in signal processing and image processing. In this paper, the characterization of the M-band symmetric orthogonal scaling function is obtained in higher dimensions. Then, a symmetric cardinal orthogonal scaling function is classified. The existing some results in one dimension are generalized to the case of higher dimensions.

Symmetric Multiwavelet Frames with General Composite Dilation in Higher Dimensions

2013 ◽  
Vol 275-277 ◽  
pp. 2669-2672
Author(s):  
Yun Sheng Huang ◽  
Xin Cheng Zhang
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Data Compression ◽  
Higher Dimensions ◽  
Sampling Theory ◽  
Processing Data ◽  
Symmetric Points

Frames play an important role in signal processing, image processing, data compression and sampling theory. In this paper, symmetric multiwavelet frames with general composite dilation and with any symmetric points are constructed from composite dilation multiwavelet frames given. At last, an example is provided to prove the theory.

Frequency domain of weakly translation invariant frame MRAs

2021 ◽  
Author(s):  
Zhihua Zhang
Keyword(s):  
Signal Processing ◽  
Data Analysis ◽  
Frequency Domain ◽  
Scaling Function ◽  
Narrow Band ◽  
Scaling Functions ◽  
Translation Invariant ◽  
Frequency Domains ◽  
Band Signal

Frequency domain of bandlimited frame multiresolution analyses (MRAs) plays a key role when derived framelets are applied into narrow-band signal processing and data analysis. In this study, we give a characterization of frequency domain of weakly translation invariant frame scaling functions [Formula: see text] with frequency domain [Formula: see text]. Based on it, we further study convex and ball-shaped frequency domains. If frequency domain of bandlimited frame scaling function [Formula: see text] is convex and completely symmetric about the origin, then it must be weakly invariant and [Formula: see text]. If [Formula: see text] has a ball-shaped frequency domain, the ball radius must be bounded by [Formula: see text]. These frequency domain characters are owned uniquely by frame scaling functions and not by orthogonal scaling functions.

The cardinal orthogonal scaling function and sampling theorem in the wavelet subspaces

2007 ◽  
Vol 194 (1) ◽  
pp. 199-214 ◽  
Author(s):  
Guo-chang Wu ◽  
Zheng-xing Cheng ◽  
Xiao-hui Yang
Keyword(s):  
Scaling Function ◽  
Sampling Theorem ◽  
Wavelet Subspaces

Matlab and Parallel Computing

2012 ◽  
Vol 17 (4) ◽  
pp. 207-216 ◽  
Author(s):  
Magdalena Szymczyk ◽  
Piotr Szymczyk
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Parallel Computing ◽  
Distributed Computing ◽  
Control Systems ◽  
High Performance ◽  
Parallel Applications ◽  
Process Simulations ◽  
Key Features ◽  
Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

scholarly journals Image Processing Architectures

2018 ◽  
pp. 68-74
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Image Quality ◽  
Comparative Study ◽  
Focal Plane ◽  
Detailed Review ◽  
Communication Problem ◽  
Fast Processing ◽  
Processing Architectures ◽  
The Comparative Study

One type of signal processing is Image processing in which the input used as an image and the output might also be an image or a set of features that are related to the image. Images are handled as a 2D signal using image processing methods. For the fast processing of images, several architectures are suitable for different responsibilities in the image processing practices are important. Various architectures have been used to resolve the high communication problem in image processing systems. In this paper, we will yield a detailed review about these image processing architectures that are commonly used for the purpose of getting higher image quality. Architectures discussed are FPGA, Focal plane SIMPil, SURE engine. At the end, we will also present the comparative study of MSIMD architecture that will facilitate to understand best one.

Sign in / Sign up

Export Citation Format

Share Document