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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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