The Multisynchrosqueezing Optimal Basic Wavelet Transform and Applications to Sedimentary Cycle Division

2021 ◽  
pp. 1-5
Author(s):  
Yajun Tian ◽  
Jinghuai Gao ◽  
Daxing Wang
Keyword(s):  
Wavelet Transform ◽  
Basic Wavelet ◽  
Sedimentary Cycle

scholarly journals Discrete Wavelet Transform on Color Picture Interpolation of Digital Still Camera

VLSI Design ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-Cheng Fan ◽  
Yi-Feng Chiang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Color Filter ◽  
Edge Weight ◽  
Discrete Wavelet ◽  
Digital Information ◽  
Color Filter Array ◽  
Image Information ◽  
Basic Wavelet ◽  
Filter Array

Many people use digital still cameras to take photographs in contemporary society. Significant amounts of digital information have led to the emergence of a digital era. Because of the small size and low cost of the product hardware, most image sensors use a color filter array to obtain image information. However, employing a color filter array results in the loss of image information; thus, a color interpolation technique must be employed to retrieve the original picture. Numerous researchers have developed interpolation algorithms in response to various image problems. The method proposed in this study involves integrating discrete wavelet transform (DWT) into the interpolation algorithm. The method was developed based on edge weight and partial gain characteristics and uses the basic wavelet function to enhance the edge performance and processes of the nearest or larger and smaller direction gradients. The experiment results were compared to those of other methods to verify that the proposed method can improve image quality.

scholarly journals Improving the reliability of the compressor unit using the wavelet transform method

2019 ◽  
Vol 140 ◽  
pp. 05013 ◽  
Author(s):  
A.A. Lebedev ◽  
A.A. Aksenov ◽  
S.M. Lebedeva ◽  
A. Yu. Petrov ◽  
Minh Hai Nguyen
Keyword(s):  
Wavelet Transform ◽  
Centrifugal Compressor ◽  
Machine Building ◽  
Oil Refining ◽  
Stable Operation ◽  
Centrifugal Compressors ◽  
Material Loss ◽  
Transform Method ◽  
Basic Wavelet ◽  
Transmission Oil

Centrifugal compressors are an integral part of modern production in such industries as gas transmission, oil refining, metallurgical, machine-building, mining, as well as in electric and heat power engineering. Interruptions in the operation or failures of compressors lead to decrease in profit or large material loss. Conditions should be created for the safe (stable) operation of centrifugal compressors. Surge is global (complete) loss of stability, an unacceptable phenomenon for a centrifugal compressor. Compressor surge protection must function during operation. The algorithms used to protect centrifugal compressors against surge have some drawbacks, which makes it impossible to reliably prevent surges. There are many methods for analyzing rapidly changing processes in the flow part of a centrifugal compressor. The wavelet theory is the most accurate and modern method. The use of the wavelet transform method for signal processing allows us to solve the problems of analyzing non-stationary processes of a centrifugal compressor to expand the acceptable range of work and build reliable operation of the anti-surge diagnostic system. In the future, it is possible to use other basic wavelet functions, for comparison and selection of the most suitable one, for the analysis of unsteady signals in a centrifugal compressor.

A Novel Adaptive Underwater Image Biorthogonal Basic Wavelet Transform De-Noising Approach

2013 ◽  
Vol 411-414 ◽  
pp. 1335-1340 ◽  
Author(s):  
Ming Wei Sheng ◽  
Yong Jie Pang ◽  
Hai Huang ◽  
Tie Dong Zhang
Keyword(s):  
Wavelet Transform ◽  
Image Restoration ◽  
Gaussian Noise ◽  
Image Process ◽  
Video Cameras ◽  
Underwater Image ◽  
Basic Wavelet ◽  
Filter Methods ◽  
Blurred Image ◽  
Contrast Image

AUVs are usually equipped with video cameras to obtain the environment underwater information. Underwater images often suffer from effects such as diffusion, scatter and caustics. In order to improve the image quality and contrast, image restoration is need to be carried out before other image process. In this paper, a novel adaptive de-noising algorithm based on multi-wavelet transform was proposed in order to remove the Gaussian noise from the blurred underwater image. Firstly, the Gaussian noise deterioration of the image model was given. Secondly, the wavelet transform algorithm using Biorthogonal as a basic wavelet for underwater image decomposition and reconstruction was presented. Finally, Haar and Biorthogonal basic wavelet were chosen separately for adaptive de-noising algorithm for the blurred image restoration. By contrast with other filter methods, the experiment results verified its useful behaviors, and demonstrate that the raised de-noising approach can achieve fairly desired de-noising effectiveness for underwater image.

A WAVELET ANALYSIS TOOLBOX FOR EXCEL

2005 ◽  
Vol 03 (01) ◽  
pp. 103-123
Author(s):  
HASSAN A. ARTAIL
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Discrete Wavelet Transform ◽  
User Interfaces ◽  
Multiresolution Analysis ◽  
Simple Technique ◽  
Visual Basic ◽  
Discrete Wavelet ◽  
Visual Basic For Applications ◽  
Basic Wavelet

This paper presents an implementation of a wavelet analysis toolbox and its integration within Excel. The toolbox includes the discrete wavelet transform, inverse wavelet transform, wavelet-based de-noising, and an associated plotting utility. The transforms and the de-noising algorithms were implemented in a DLL using C++ while the user interfaces were developed using Visual Basic for Applications (VBA) forms. A simple technique was used to automate the transfer of data, associated properties, and user selections from the worksheet to the DLL, and the computed values from the DLL back to the worksheet. We show how the grouping and presentation of computed wavelet coefficients allow for multiresolution analysis and further processing within Excel. We highlight the benefits behind implementing the basic wavelet analysis functions in Excel with reference to MATLAB.

An uncertainty principle for the basic wavelet transform

2021 ◽  
pp. 2150002
Author(s):  
Javid Ahmad Ganie ◽  
Renu Jain
Keyword(s):  
Quantum Mechanics ◽  
Wavelet Transform ◽  
Weight Function ◽  
Uncertainty Principle ◽  
Signal Analysis ◽  
Integral Transform ◽  
Generalized Uncertainty Principle ◽  
Basic Wavelet ◽  
And Performance ◽  
Definition Of

The aim of this paper is to derive the uncertainty principle which has implications in signal analysis and in quantum mechanics. First, we derive the definition of the wavelet transform in [Formula: see text]-calculus by using some weight function. Certain properties like linearity, scaling, translation, etc. were discussed. Later on, to illustrate this integral transform several results were derived for the effectiveness and performance of the proposed method. Also, this paper surveys recent applications to establish generalized uncertainty principle.

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