Download pdf - lifting wavelet tranform project

8/2/2019 lifting wavelet tranform project

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Image fusion algorithm using

lifting wavelet transform

Project work by:

PREETHAM .K P

PRAMOD. M .SUGNANAMURTHY

NIRANJAN. N.TANTRY

NAGESH SHENOY. Y D

Guided by:D. N. Krishna Kumar

Asst. Professor

BNMIT


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Introduction Image fusiona technique that integrates complementary information from

multiple image sensor data such that the new image is more suitable for

processing tasks.

Type

s -

Pixel level fusion : Generates a fused image in which information associated with

each pixel is determined from a set of pixels in source images.

Feature level fusion : Requires the extraction of salient features which are

depending on their environment such as pixel intensities, edges or textures. Thesesimilar features from input images are fused.

Decision level fusion : Is a higher level of fusion. Input images are processed

individually for information extraction. The obtained information is then combined

applying decision rules to reinforce common interpretation.


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Introduction (cont)

y Wavelets - mathematical functions that cut up data

into different frequency components, and then study

each component with a resolution matched to its

scale. They have advantages over traditional Fourier

methods in analyzing physical situations where

the signal contains is continuities and sharp spikes.

y DWT - discrete variant of the wavelet transform. The DWT of

images is a transform based on the tree structure with D levels that

can be implemented by using an appropriate bank of filters.


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Existing System

y Fusion framework in feature-level.

y Self-Organizing Neural Network.

y Fuzzy Logic Neural Networks


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Proposed Systemy An algorithm for image fusion using Lifting Wavelet filter is

proposed.

y In this algorithm, fusion is performed in transformed domain.

y The performance of this algorithm is compared with that obtained

using Laplacian Pyramid, Averaging fusion based approaches and

guidelines on selection of an appropriate image fusion algorithm for

different sensor conditions are evolved


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Lifting Wavelet Transform

y Lifting Wavelet Transform is similar to DWT except that the number of

samples at each stage is same as the initial set of samples. The input

samples are split into odd and even sets of samples and passed through

the filters (lifting steps) to give rise to approximation and details.

y

The lifting scheme is a technique for both designing wavelets andperforming the DWT. Actually it is worthwhile to merge these steps and

design the wavelet filters while performing the wavelet transform. This is

then called the second generation wavelet transform.


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Block Diagram


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Functional Flow Diagrams


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Applicationsy Aerial and Satellite imaging

y Robot vision

y Concealed weapon detection

y Multi-focus image fusion

y Digital camera application

y Battle field monitoring


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Applications (cont)

yMedical imaging -

Fusing X-ray computed topography (CT) and

magnetic resonance (MR) images.

Computer assisted surgery.

Spatial registration of 3-D surface.

yReal time applications -

Deployed to make geographical data live GOOGLE MAPS or

WIKIMAPIA.


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Advantages

y Because of the trade off between spatial resolution and spectral

resolution in satellite imagery, it is often desirable to fuse lower

resolution multispectral imagery with a high-resolution

panchromatic image in order to obtain an image with the

spectral resolution and quality of the former and the spatial

resolution and quality of the latter.

y Older algorithms tend to distort the color information.

y The results from wavelet-based methods can be improved by

applying more sophisticated schemes or more advanced models

for injecting detail information.


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Tools The basic algorithm is implemented using MATLAB r2009a running

on a computer system.

MATLAB (matrix laboratory) is a numerical computing environment

and fourth-generation programming language.

Developed by Mathworks, it has a large number of inbuilt toolboxes.

We use the following ones

Image Processing Toolbox

Wavelet Toolbox

Image Acquisition Toolbox

Mapping Toolbox


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Conclusion

y The proposed algorithm chooses a different rule to fuse the image.

y It is compared with Laplacian pyramid, the traditional low

frequency and "average" algorithm.

y It executes fast and saves memory.

y Details and the edge of the fused image are reserved better.

y The algorithm is very effective and is able to fuse multi-source

images.


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