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.

The fusion of images is often required for images acquired from different instrument modalities or capture techniques of the same scene or objects.

Image fusion is the process by which two or more images are combined into a single image retaining the important features from each of the original images.

FUSION METHODS

Linear superposition Nonlinear methods Optimization approaches Artificial neural networks Image pyramids Wavelet transform Generic multiresolution fusion scheme

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.

Literature survey

The technique that was used before was called multi resolution analysis

Existing System

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

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.

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.

1D Discrete Wavelet Transform

2D Discrete Wavelet Transform

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.

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

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

DWT Sub-band Structure

Stage 1

Stage 2

Stage 3

DWT with D=3 stages

A DWT Example

LL1

HH1HL1

LH1

HH2

LH2HH2

LH

2

HL

2

HL2

LL2

LL0

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

Functional Flow Diagram 2

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).

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.

GUI - EXISTING TECHNIQUES

GUI – WAVELET APPROACH

GUI – FUZZY BASED

GUI – WAVELET AND FUZZY BASED

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)

Applications

NAVIGATION AID

MEDICAL IMAGING

REMOTE SENSING

MERGING OUT-OF-FOCUS IMAGES

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

Applications

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

• Computer assisted surgery

• Spatial registration of 3-D surface

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

Applications

Military and law enforcement

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

• Concealed weapon detection

• Battle-field monitoring

• Night pilot guidance

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

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.