This document discusses using singular value decomposition (SVD) for image fusion. SVD is used to extract pixel intensities from low quality input images to create a higher resolution, more informative output image. The algorithm involves reading input images, converting them to grayscale, performing SVD, fusing the decomposed matrices using weighting factors, and calculating performance metrics like mean squared error and peak signal-to-noise ratio to evaluate the output image compared to a reference image. The results suggest SVD-based image fusion effectively retrieves relevant information from the input images.

1. 1
SVD BASED IMAGE FUSION
SREELEKSHMY SELVIN
M.TECH CEN
P2.CEN.15021
SREELEKSHMY SELVIN
M.TECH CEN
AMRITA SCHOOL OF ENGINEERING

2. ABSTRACT
 The objective of this work is to demonstrate
the application of Singular Value
Decomposition for fusing images. Image
fusion yields an output image which is
obtained as a combination of two or more
low quality input images. The output image
will have more resolution and is more
informative. Variations in data values are
best analyzed by using SVD; hence in this
work we make use of SVD to extract the
best pixel intensities.
SREELEKSHMY SELVIN
M.TECH CEN
AMRITA SCHOOL OF ENGINEERING 2

3. INTRODUCTION
 Image fusion : process of combining two
or images with same background.
 It has got applications in the areas like
remote sensing, medical image analysis.
 Can be performed in various levels such
as
pixel level
signal level
feature level
decision level
SREELEKSHMY SELVIN
M.TECH CEN
AMRITA SCHOOL OF ENGINEERING 3

4. CONT..
 SVD make use of pixel level image fusion
 The various image fusion techniques
include
1) High pass filtering technique
2) IHS transform based image fusion
3) PCA based image fusion
4) Wavelet transforms image fusion
5) Pair-wise spatial frequency matching
SREELEKSHMY SELVIN
M.TECH CEN
AMRITA SCHOOL OF ENGINEERING 4

5. ALGORITM
 READ INPUT IMAGES
 READ THE REFERENCE IMAGE
 CONVERT ALL IMAGES TO GRAYSCALE
 FIND SVD OF INPUT IMAGES
 FIND THE PROMINENT VALUES FROM
THE S VALUES OF BOTH IMAGES
SREELEKSHMY SELVIN
M.TECH CEN
AMRITA SCHOOL OF ENGINEERING 5

6. CONT…
 FIX THE PARAMETERS K,W
 EXTRACT THE 'K' PROMINENT VALUES
FROM THE DECOMPOSED MATRICES
 FUSE THE IMAGE AND APPLY
WEIGHING FACTOR
 DISPLAY THE OUTPUT IMAGE AND
REFERENCE IMAGE
 FIND THE PERFORMANCE FACTORS
SREELEKSHMY SELVIN M.TECH CEN AMRITA
SCHOOL OF ENGINEERING 6

7. INPUT IMAGES
SREELEKSHMY SELVIN
M.TECH CEN
AMRITA SCHOOL OF ENGINEERING 7

8. OUTPUT IMAGE &
REFERENCE IMAGE
SREELEKSHMY SELVIN
M.TECH CEN
AMRITA SCHOOL OF ENGINEERING 8
OUTPUT IMAGE REFERENCE IMAGE

9. PERFORMANCE
FACTORS
 The performance factors gives the
measure of similarity between the output
image and the reference image
 Mean Square Error (MSE)
 average of squares of error
 image quality increases as MSE
decreases
SREELEKSHMY SELVIN
M.TECH CEN
AMRITA SCHOOL OF ENGINEERING 9

10. CONT…
 PEAK SIGNAL TO NOISE RATIO
(PSNR)
 ratio between the maximum possible power of
a signal and the power of noise that affects
the output.
 image quality increases as PSNR increases.
SREELEKSHMY SELVIN
M.TECH CEN
AMRITA SCHOOL OF ENGINEERING 10

11. CONT..
NORMALISE
CROSSCORRELATION
(NC)
 measure of similarity of
two images.
 image quality increases
as NC increases.
 STRUCTURED
CONTENT (SC)
 ratio between the content of
both the expected and the
obtained data
 Practically it is the square of
the expected data and the
net sum of square of the
obtained data.
SREELEKSHMY SELVIN
M.TECH CEN
AMRITA SCHOOL OF ENGINEERING 11

12. CONT…
 NORMALISED
ABSOLUTE ERROR
(NAE)
 the net sum ratio
between the error
values and the
perfect values is
calculated.
SREELEKSHMY SELVIN
M.TECH CEN
AMRITA SCHOOL OF ENGINEERING 12

13. RESULT
 SVD based image fusion retrieves the
relevant information from the singular
values and performs fusion.
 It is more effective than the other
existing methods.
SREELEKSHMY SELVIN
M.TECH CEN
AMRITA SCHOOL OF ENGINEERING 13

14. SREELEKSHMY SELVIN M.TECH CEN AMRITA
SCHOOL OF ENGINEERING 14