1. MAIN PROJECT IMAGE FUSION USING MATLAB
D.Naresh babu(08R21A0427)
S.Siddartha(08R21A0450)

2. 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.

3. 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.

4. 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.

5. 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.

6. Advantages
 No need to divide the input coding into non-overlapping2-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 scaling
Better identification of which data is relevant to human perception
higher compression ratio(64:1 vs. 500:1)

7. Applications
NAVIGATION AID
MEDICAL IMAGING
REMOTE SENSING

8. THANK YOU