IMAGE FUSION:
Image Fusion is a process of combining the relevant information
from a set of images of the same scene into a single image and the resultant
fused image will be more informative and complete than any of the input images.
Input images could be multi sensor, multimodal, multi focus
or multi temporal. There are some important requirements for the image fusion
process:
- The fused image should preserve all relevant information
from the input images
- The image fusion should not introduce artifacts which can
lead to a wrong diagnosis
One of the important pre-processing steps for the fusion
process is image registration. Image registration is the process of
transforming different sets of data into one coordinate system. Image fusion
find application in the area of navigation guidance, object detection and recognition,
medical diagnosis, satellite imaging for remote sensing, military and civilian
surveillance, etc. Image fusion algorithms can be categorized into different
levels: pixel, feature, and decision levels. Pixel level fusion works directly
on the pixels of source images while feature level fusion algorithms operate on
features extracted from the source images.
PCA (Principal Component
Analysis) Based Image Fusion:
Principal Component Analysis is a sub space method, which
reduces the multidimensional data sets into lower dimensions for analysis. This
method determines the weights for each source. Image using the eigenvector corresponding
to the largest Eigen value of the covariance matrix of each source image.
Fig: Block Diagram of PCA based Fusion
Discrete Wavelet Transform
Method:
Wavelet transforms
are multi-resolution image decomposition tool that provide a variety of
channels representing the image feature by different frequency sub bands at
multi-scale. It is a famous technique in analyzing signals. When decomposition
is performed, the approximation and detail component can be separated 2-D
Discrete Wavelet Transformation (DWT) converts the image from the spatial
domain to frequency domain. The image is divided by vertical and horizontal
lines and represents the first-order of DWT, and the image can be separated
with four parts those are LL1, LH1, HL1 and HH1.
Let
s(n1,n2) is input image with size N1xN2 then scaling and wavelet function are
Steps:
1.
Take input images of same size and of same scene or object taken from different
sensor like visible and infra red images or images having different focus.
2.
If the input images are color, separate their RGB planes to perform 2D
transforms.
3.
Apply one of the different image fusion technique.
4.
Fuse the input image components by taking any of the pixels merging technique.
5.
Resulting fused transform components are converted to image using inverse
transform.
MATLAB Implementation of
Image Fusion:
GUI Implementation: 1 Original View..............
GUI Implementation: 2 Image Fusion using Stationary Wavelet Transform..............
GUI Implementation: 3 Image Fusion with All Methods.........
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sir plz provide matlab code for implementation of fusion using PCA, stationary and wavelet transform
ReplyDeleteI need code
ReplyDeleteSir please provide me the code for Fusion of remote sensing images
ReplyDeletemy mail id is kulvaibhavsharma@gmail.com
kinldy send me this code sir please my email id is
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stone7906@gmail.com
ReplyDeletematlab code required urgent
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ReplyDeleteHello. I need this code in my researcher. thanks
ReplyDeletethis my email: kazim_201082@yahoo.com
Would appreciate your code to experiment on fusing IT and visible images.
ReplyDeleteSincerely;
Jim Reeves
Meant IR (thermal)
DeleteI need the code please help
ReplyDeletemy email:
abinashkumarray@gmail.com
Thank you