An Introduction to Wavelet-Based Image Processing and Its Applications

2018 ◽  
pp. 110-128 ◽  
Author(s):  
Mahesh Kumar H. Kolekar ◽  
G. Lloyds Raja ◽  
Somnath Sengupta
Keyword(s):  
Wavelet Transform ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Jpeg 2000 ◽  
Parent Child Relationship ◽  
Child Relationship ◽  
Multi Resolution Analysis ◽  
Low Pass ◽  
Multiple Resolutions ◽  
Resolution Analysis

This chapter gives a brief introduction of wavelets and multi-resolution analysis. Wavelets overcome the limitations of Discrete Cosine Transform and hence found its application in JPEG 2000. In wavelet transform, the scaling functions provide approximations or low-pass filtering of the signal and the wavelet functions add the details at multiple resolutions or perform high-pass filtering of the signal. Applying Discrete Wavelet Transform to an image decomposes it into LL, LH, HL, and HH subbands. The low frequency LL band carries most of the significant information in the image. Wavlet transform allows us to analyse the local properties of a signal or image by shifting and scaling operations. The inherent properties of wavelets makes it useful in image denoising, edge detection, image compression, compressed sensing and illumination normalization. The wavelet coefficients at various levels of decomposition follows a parent-child relationship.

An Introduction to Wavelet-Based Image Processing and its Applications

2014 ◽  
pp. 38-53
Author(s):  
Mahesh Kumar H. Kolekar ◽  
G. Lloyds Raja ◽  
Somnath Sengupta
Keyword(s):  
Wavelet Transform ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Jpeg 2000 ◽  
Parent Child Relationship ◽  
Child Relationship ◽  
Multi Resolution Analysis ◽  
Low Pass ◽  
Multiple Resolutions ◽  
Resolution Analysis

This chapter gives a brief introduction of wavelets and multi-resolution analysis. Wavelets overcome the limitations of Discrete Cosine Transform and hence found its application in JPEG 2000. In wavelet transform, the scaling functions provide approximations or low-pass filtering of the signal and the wavelet functions add the details at multiple resolutions or perform high-pass filtering of the signal. Applying Discrete Wavelet Transform to an image decomposes it into LL, LH, HL, and HH subbands. The low frequency LL band carries most of the significant information in the image. Wavlet transform allows us to analyse the local properties of a signal or image by shifting and scaling operations. The inherent properties of wavelets makes it useful in image denoising, edge detection, image compression, compressed sensing and illumination normalization. The wavelet coefficients at various levels of decomposition follows a parent-child relationship.

A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 505
Author(s):  
Y Srinivasa Rao ◽  
G. Ravi Kumar ◽  
G. Kesava Rao
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
New Approach ◽  
The Neural Network ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

Wavelet analysis on developable surface base on area preserving projection

2015 ◽  
Vol 13 (01) ◽  
pp. 1550007 ◽  
Author(s):  
Bao Qin Wang ◽  
Gang Wang ◽  
Xiao Hui Zhou ◽  
Yu Su
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Scaling Function ◽  
Discrete Wavelet ◽  
Developable Surface ◽  
Simple Method ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Area Preserving ◽  
The Scaling Function

In this paper, a simple method is given in order to construct an area preserving mapping from a developable surface M to a plane. Based on the area preserving projection, we give some important formulas on M, and define a multi-resolution analysis on L2(M). We provide the conditions to further discuss the continuous wavelet transform and discrete wavelet transform on developable surface. At the same time, we derived two-scale equations that the scaling function and wavelet function on developable surface satisfied, we also define and discuss the orthogonality, and several important theorems are given. Finally, we construct the numerical examples. The focus of this paper is the area preserving mapping that from developable surface M to a plane, and the discrete wavelet transform on developable surface.

A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 505
Author(s):  
Y Srinivasa Rao ◽  
G. Ravi Kumar ◽  
G. Kesava Rao
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
New Approach ◽  
The Neural Network ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

scholarly journals Efficient Image Watermarking Using Filtered DWT-Blocks for Quantization of Significant Differences

2014 ◽  
Vol 4 (1-2) ◽  
Author(s):  
Thien Huynh-The ◽  
Thuong Le-Tien ◽  
Tuan Nguyen-Thanh
Keyword(s):  
Image Watermarking ◽  
Histogram Equalization ◽  
Adaptive Threshold ◽  
Parent Child Relationship ◽  
Child Relationship ◽  
Low Pass ◽  
The Difference ◽  
Block Based ◽  
Robust Blind Watermarking ◽  
Relationship Of

In the paper, a robust blind watermarking method is introduced for gray-scale images based on wavelet tree quantization with an adaptive threshold in the extraction. Every block of 2×2 coefficients of High-Low subbands of the Wavelet tranform are grouped in a block through the parent-child relationship of the wavelet tree. Every scrambled binary watermark bit is embedded into each block based on the difference value of two largest coefficients. The watermark is recovered by comparing the difference values in each block to an adaptive threshold. The accuracy of an extracted watermark depends on the threshold which is determined by minimizing the sum of weighted within-class variance. The performance of the proposed watermarking method is represented through experimental results under various types of attack such as, Histogram Equalization, Cropping, Low-pass Filtering, Gaussian noise, Salt & Pepper noise and JPEG compression. In additions, the proposed method is also compared to recent methods in the extraction performance.

scholarly journals A Low Power Shift Add Multiplier for Lifting Based Dwt using Kogge Stone Adder

2020 ◽  
Vol 9 (4) ◽  
pp. 1080-1086
Keyword(s):  
The Other ◽  
Discrete Wavelet ◽  
Limited Memory ◽  
Power Shift ◽  
Multi Resolution Analysis ◽  
Suggested Technique ◽  
Field Programmable ◽  
Resolution Analysis ◽  
Common Technique

The most common technique used for image processing applications is ‘The wavelet transformation’. The Discrete Wavelet Transform (DWT) keeps the time as well as frequency information depend on a multi resolution analysis structure, where the other classical transforms like Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) will not do that. Because of this feature, the quality of the repaired image is improved when comparing to the other transforms. To implement the DWT on a real time codec, a fast device needs to be targeted. While comparing with the other implementation such as PCs, ARM processors, DSPs etc, Field Programmable Gate Array (FPGA) implementation of DWT had better processing speed and costs were vey less. A Fast Architecture based DWT using Kogge Stone Adder is proposed in this paper where the coefficients of lifting scheme are calculated by using Shift adder and Kogge Stone Adder where other techniques used multiplier. The most important intention of the suggested technique is to use minimum calculation and limited memory. The simulation of the suggested design is dole out on the Xilinx 14.1 style tool and also the performance is evaluated and compared with the present architectures.

Voltage Flicker Extraction Based on Wavelet Analysis

2013 ◽  
Vol 385-386 ◽  
pp. 1389-1393 ◽  
Author(s):  
Lin Chai ◽  
Jun Ru Sun
Keyword(s):  
Low Frequency ◽  
Matlab Simulation ◽  
Time Frequency ◽  
Multi Resolution Analysis ◽  
Voltage Signal ◽  
Time Frequency Localization ◽  
Resolution Analysis ◽  
Modulus Maximum ◽  
Voltage Flicker ◽  
Non Stationary Signal

Extracting voltage flicker from the sampling voltage signal is a precondition for management of flicker. Voltage flicker signal is a low frequency time-varying non-stationary signal. The traditional fourier transform has great limitations when analyze the non-stationary signal for not having the time resolution. As wavelet transform has good property of time-frequency localization, it become a powerful tool for analyze this kind of signal. This paper adopts multi-resolution analysis of wavelet to extract voltage flicker signal. Furthermore, according to the characteristics of wavelet function, this paper selects Daubechies wavelet to accomplish the multi-level decomposition and reconstruction of signal, in order to get the frequency and amplitude of voltage flicker signals. Based on the principle of modulus maximum, it can be determined what time the voltage flicker happen and over. The results of MATLAB simulation indicate that voltage flicker signal can be effectively extracted by wavelet multi-resolution analysis. Wavelet multi-resolution analysis is considerably ideal for voltage flicker extraction.

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