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IMAGE FUSION USING DWT Main Project Presentation

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Page 1: IMAGE FUSION ppt

IMAGE FUSION USING DWT

Main Project Presentation

Page 2: IMAGE FUSION ppt

Introduction

Image fusion – a technique that integrates complementary information

from multiple image sensor data such that the new image are more suitable for processing tasks.

An image pyramid can be described as collection of low- or band-pass copies of an original image in which both the band limit and sample density are reduced in regular steps.

The basic strategy of image fusion based on pyramids is to use a feature selection rule to construct a fused pyramid representation from the pyramid representations of the original data. The composite image is obtained by taking an inverse pyramid transform.

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DECISION RULE BASED IMAGE FUSION USING WAVELET

TRANSFORM

In recent years, many solutions to image fusion have been proposed. This paper presents an effective multi-resolution image fusion methodology, which is wavelet based image fusion. Fusion process is applied in the clinical case: the study of some particular

disease by MR/SPECT fusion. The effectiveness of the proposed model is demonstrated via results comparison with several other image fusion methods.

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Literature survey

The technique that was used before was called multi resolution analysis

Existing System

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Existing System

Fusion framework in feature-level. Effective multi-sensor image data fusion methodology on

the basis of discrete wavelet transform theory Self-Organizing Neural Network.

Proposed System

Fusion framework in Decision level

Using discrete wavelet transform method

Fuzzy logic Neural Networks

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Wavelet Transform

What is wavelet Transform: Wavelet Transform is a type of signal

representation that can give the frequency content of the signal at a particular instant of time.

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Wavelet Transform

Why need wavelet transform? Wavelet analysis has advantages over

traditional Fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes.

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Discrete Wavelet Transform(DWT)

Wavelet transform is related to two functions:the scaling function Ø(x) and mother wavelet ψ(x).

hi, gi are called coefficients(filters).

hi : Smooth filters.(Lowpass filters)

gi : Detail filters.(Highpass filters)

Each kind of wavelet transform defines its own filter coefficients. E.g Daubechies.

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1D Discrete Wavelet Transform

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2D Discrete Wavelet Transform

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Fuzzy Logic Definition

“Fuzzy logic a superset of Boolean logic dealing with the concept of

partial truth – truth values between ‘completely true’ and ‘completely

false’.It was introduced mainly to model the uncertainty of natural

language.”

Fuzzy set to describethe degree to whichtwo numbers are

similar, for example,degree of similarityof temperatures

0.000.250.500.751.00

-20 -10 0 10 20difference (°C)

very

slightlyquite

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New Approach

Discrete wavelet transform can offer a more precise way for image analysis.It decomposes a image into low frequency band and high frequency band in different levels, and it can also be reconstructed at these levels.

When images are merged in this method different frequencies are processed differently.

Improves the quality of the new image since it works on Feature extraction.

The fusion algorithm is performed at the pixel level.

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DWT Sub-band Structure

L

H

2L

H

L

H

Horizontal(Rows) Vertical(Columns)

N/2 x MN/2 x M/2

LL

LH

HL

HH 2

2

2

2

2

Image with resolution Level R

N x M

L: Lowpass filter

H: Highpass filter

2: downsample by 2

Detail Image corresponding to information visible at the resolution Level R

Image corresponding to resolution Level R-1

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DWT Sub-band Structure

LL: Horizontal Low pass& Vertical Low pass

LH: Horizontal Low pass& Vertical High pass

HL: Horizontal High pass& Vertical Low pass

HH: Horizontal High pass& Vertical High pass

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DWT Sub-band Structure

Stage 1

Stage 2

Stage 3

DWT with D=3 stages

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A DWT Example

LL1

HH1HL1

LH1

HH2

LH2HH2

LH

2

HL

2

HL2

LL2

LL0

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Functional Flow Diagram

Input Image A

Wavelet decomposition

Filtering in the domain of spatial frequency

Fusion Rules

Fusion Decision MapInverse wavelet decomposition

Fused Image

Input Image B

Image reconstruction

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Functional Flow Diagram 2

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Tools

MATLAB 6.5

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Implementation

Relevant wavelet theory

Since image is 2-D signal, we will mainly focus on the 2-D wavelet transforms.

After one level of decomposition, there will be four frequency bands, namely Low-Low (LL), Low-High (LH), High-Low (HL) and High-High (HH).

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Implementation

The next level decomposition is just apply to the LL band of the current decomposition stage, which forms a recursive decomposition procedure.

The frequency bands in higher decomposition levels will

have smaller size.

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Implementation

The wavelet transforms w of the two registered input images are computed and these transforms are combined utilizing some kind of fusion rule

The fusion rule is actually a set of fusion rules,where j= 1, .., J and c = 1,. . . ,7, which define the fusion of each pair of corresponding channels for each band

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GUI - EXISTING TECHNIQUES

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GUI – WAVELET APPROACH

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GUI – FUZZY BASED

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GUI – WAVELET AND FUZZY BASED

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Advantages

No need to divide the input coding into non-overlapping 2-D blocks, it has higher compression ratios avoid blocking artifacts.Allows good localization both in time and spatial

frequency domain.Transformation of the whole image introduces

inherent scalingBetter identification of which data is relevant to

human perception higher compression ratio (64:1 vs. 500:1)

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Applications

NAVIGATION AID

MEDICAL IMAGING

REMOTE SENSING

MERGING OUT-OF-FOCUS IMAGES

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Applications

Intelligent robots

•Require motion control, based on feedback from the environment from visual, tactile, force/torque, and other types of sensors •Stereo camera fusion •Intelligent viewing control •Automatic target recognition and tracking

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Applications

Medical image•Fusing X-ray computed topography (CT) and magnetic resonance (MR) images

• Computer assisted surgery

• Spatial registration of 3-D surface

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Applications

Manufacturing

• Electronic circuit and component inspection • Product surface measurement and inspection

non-destructive material inspection • Manufacture process monitoring • Complex machine/device diagnostics • Intelligent robots on assembly lines

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Applications

Military and law enforcement

• Detection, tracking, identification of ocean (air,ground)target/event

• Concealed weapon detection

• Battle-field monitoring

• Night pilot guidance

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References

BASE PAPER : DAVID L. HALL and JAMES LLINAS, An Introduction to Multisensor Data Fusion, Proceedings of IEEE, 85, 1 (Jan.

1997) Barbara Zitova, Jan Flusser, Image registration methods: a survey. Image and Vision Computing 21

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References

RELATED PAPERS : L. J. Chipman and T. M. Orr, “Wavelets and image fusion,” in Proceedings of the IEEE International Conference on Image Processing, Washington D.C., October 1995, pp. 248– 251 (2003) L.J. Chipman, T.M. Orr, and L.N. Lewis. Wavelets and image

fusion.IEEE Transactions on Image Processing, 3:248–251, 1995. linage fusion techniqcs Sinione.Giovanni and Farina. Alfonso and

Morahito. Francesco and Scmico. Sebastiano Bruno and Bruzzone.Lorcnzo (U). Technical Report DIT-02-025, University of Trento.