wavelet coding

8/7/2019 wavelet coding

http://slidepdf.com/reader/full/wavelet-coding 1/25

D.Ravikrishna Reddy M.Tech, (P.hD)

Presented by

B.RAJESH BABU 07X91A0429

Y.MAHENDRA 08X95A0404

V.VENKATA RAO 07X91A0457

R.SUDHEER 07X91A0443

IMAGE FUSION BY WAVELET METHOD

Under the esteemed guidance of



OBJECTIVE:

The fusion image is the process of combining two

or more images into a single image. Wavelet transformfusion is the most common form of transform image

fusion.

Image fusion improves geometric corrections and

sharpness the image. It also replace the data.



Digital image processing:

An image may be defined as a two dimensional function f(x,y).where x,y

are the spatial coordinates

The amplitude of f at pair of coordinates (x,y) is called intensity or gray

level of image

A gray scale image is a function I(x,y) of two spatial coordinates of the

image plane

I(x,y) f the is the intensity of the image at point (x , y)



FOURIER AND WAVELETANALYSIS OF IMAGES:

Fourier analysis:

Fourier analysis is a mathematical technique for transforming our view of

the signal from time based to frequencey based.

It is a serious draw back, in transforming to the frequency domain time

information lost.



Short-Time Fourier Analysis:

Dennis Gabor (1946) adapted the Fourier transform to analyze only a small

section of the signal at a time²a technique called windowing the signal.

Gabor¶s adaptation, called the Short-Time Fourier Transform (STFT), maps

a signal into a two-dimensional function of time and frequency



Wavelet Analysis:y A wavelet is a waveform of effectively limited duration that has an average

value of zero.

y Sinusoids do not have limited duration ² they extend from minus to plus

infinity. And where sinusoids are smooth and predictable, wavelets tend to

be irregular and asymmetric.



Wavelet Analysis R epresentation:

Wavelet analysis allows the use of long time intervals where we want more

precise low-frequency information, and shorter regions where we want

high-frequency information.



Fig. Time, Frequency, STFT & Wavelet Domain Representations



The Continuous Wavelet Transf orm:

which is the sum over all time of the signal f(t) multiplied by a complexexponential.



What¶s continuous about the continuous wavelet transform:

The CWT is also continuous in terms of shifting during computation, the

analyzing wavelet is shifted smoothly over the full domain of the analyzed

function.



Discrete WaveletTransf orm:

An efficient way to implement this scheme using filters was developed in

1988 by Mallat.

Calculating wavelet coefficients at every possible scale is a fair amount of

work, and it generates an awful lot of data.

F

or many signals, the low-frequency content is the most importantpart. It is what gives the signal its identity.



One-Stage Filtering:

Fig.1 One stage filtering Fig.2 Example For One Stage Filtering



Multiple-Level Decomposition:

Fig. Multilevel Decomposition Fig. Wavelet Decomposition Tree

The decomposition process can be iterated, with successive approximations

being decomposed in turn, so that one signal is broken down into many lower

resolution components. This is called the wavelet decomposition tree.



WaveletR econstruction:

Fig. Samples R epresentation

Discrete wavelet transform can be used to analyze or decompose signals and

images.

Up sampling is the process of lengthening a signal component by inserting

zeros between samples:



R econstruction Filters:

Up sampling is the process of lengthening a signal component by insertingzeros between samples.



Multi-step Decomposition and R econstruction:



IMAGE FUSION:

Objectives of Image Fusion Schemes:

Related Research Fields of Image Fusion:yComputer Vision

yAutomatic object detection

yImage processing

yRobotics

Fusion Rules

Input images need to be combined to form a new set of coefficients to beused for backward transform.



Implementing wavelet transf orm:

Wavelet transform based image fusion involves three steps; forward transform,

coefficient combination and backward transform.



Different wavelets used in the image f usion:

Inputs are

1.multispectral image

2.panchromatic image

Fig. Fusion of the wavelet transf orms of two images.



Wavelet Decomposition:

Images are treated as two-dimensional signals, they change horizontally and

vertically, and thus 2D wavelet analysis must be used for images.



Deco ositio of S tur Image to level :



R esult:

Panchromatic image Multispectral image Fused image using orthogonal wavelet







THANK YOU

wavelet coding

Documents