Fixed-point DSP implementation of wavelet decomposition for image processing

2005 ◽  
Author(s):  
G. Frassi ◽  
A. Bellini
Keyword(s):  
Image Processing ◽  
Fixed Point ◽  
Wavelet Decomposition ◽  
Dsp Implementation

Fixed-point DSP implementation of nonlinear controller for large gap electromagnetic suspension system

2009 ◽  
Vol 17 (10) ◽  
pp. 1148-1156 ◽  
Author(s):  
Paulo H. da Rocha ◽  
Henrique C. Ferreira ◽  
Michael C. Porsch ◽  
Roberto M. Sales
Keyword(s):  
Fixed Point ◽  
Suspension System ◽  
Nonlinear Controller ◽  
Electromagnetic Suspension ◽  
Dsp Implementation

A DSP implementation of lifting based DWT for image processing applications

2011 ◽  
Author(s):  
Morteza Gholipour ◽  
Hossein A. Noubari ◽  
Mahmoud Kamarei
Keyword(s):  
Image Processing ◽  
Dsp Implementation

DSP IMPLEMENTATION OF THE MIMO MC-CDMA RECEIVER USING THE FIXED POINT CORDIC ALGORITHM

2018 ◽  
Vol 18 (8) ◽  
pp. 1203-1218
Author(s):  
Youness Mehdaoui ◽  
Rachid El Alami ◽  
Mostafa Mrabti
Keyword(s):  
Fixed Point ◽  
Cordic Algorithm ◽  
Dsp Implementation ◽  
Cdma Receiver

Simultaneous Least Squares Wavelet Decomposition for Multidimensional Irregularly Spaced Data

2012 ◽  
Vol 239-240 ◽  
pp. 1213-1218 ◽  
Author(s):  
Mehdi Shahbazian ◽  
Saeed Shahbazian
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Wavelet Decomposition ◽  
Least Square ◽  
Multidimensional Case ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Wavelet Coefficients ◽  
Sampled Data ◽  
Irregularly Sampled Data

The multidimensional Discrete Wavelet Transform (DWT) has been widely used in signal and image processing for regularly sampled data. For irregularly sampled data, however, other techniques should be used including the Least Square Wavelet Decomposition (LSWD). The commonly used level by level (sequential) wavelet decomposition, which calculates the wavelet coefficients in each resolution separately, may result in a gross interpolation error. To overcome this drawback, a different approach called the Simultaneous Least Square Wavelet Decomposition, which computes all wavelet coefficients simultaneously, have been proposed by the authors. In this paper, we extend the simultaneous LSWD approach to the multidimensional case and show that this method has excellent reconstruction property for two dimensional irregularly spaced data.

scholarly journals A Contraction Mapping Method in Digital Image Processing

2020 ◽  
Vol 8 (4S5) ◽  
pp. 193-196
Keyword(s):  
Image Processing ◽  
Fixed Point ◽  
Fixed Point Theorem ◽  
Digital Image Processing ◽  
Point Theorem ◽  
Digital Image ◽  
Contraction Mapping ◽  
Computational Technique ◽  
Contraction Mappings ◽  
Topological Features

The topological features of the objects or digital image pictures are characterized by digital topology. A digital image picture is a distinctive arrangement of numbers which are non negative. Decomposing an image picture into its constituent components and investigating its several characteristics with fundamental elements is generally coined as digital image processing. In investigating the fundamental constituents of images, the separation of connected segments are established to enquire the adjacency relationship. During this course of tracking, thinning and coding them, it is kept in mind that the specification of connectedness of the pictures remains unaltered. The characteristics of the constituent subsets and relationships may be stipulated when the image is fragmented into its elementary constituents. Some features of the subsets of these constituent parts are based on their respective positions. Thus, the basic idea for image processing is the primary topological characteristics of digital images like adjacency, connectedness, etc. The fixed point theorems associated to certain kinds of contraction mappings can be utilised in the field of engineering science and technology as computational technique to provide an exclusive programme to explore various problems. Parallel and distributed computation, modeling, simulation and digital image processing are few notables among these techniques. In digital image processing digital contraction mapping is defined and then existence of the solution and its uniqueness is obtained using the fixed point theorem concerned, which is the mathematical basis of border following, thinning and contour filling of a digital image picture. In digital image processing the applicability of fixed point theorem and contraction mappings as a computational technique has been grazed well. To further broaden its pertinency in image processing our interest is to delve into some of contraction mappings as a significant mechanism.

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