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I.
II.
III.
IV.
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ii.
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Image fusion
Fusion techniques
Literature survey
Proposed techniques
Mask
Rectangular mask
Triangular mask
Fan shaped mask
Strip mask
Image fusion in transform domain using masking
Performance evaluation of image fusion techniques.
Signal to noise ratio error
Root mean square
Result of existing technique
Comparison of image fusion using different mask.
Conclusion
References
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Image fusion combines multiple images of the
same scene into a single image which is suitable
for human perception and practical applications.
Image fusion is applicable for numerous fields
including: defence systems, remote sensing and
geosciences, robotics and industrial engineering, and
medical imaging.
The most important issue concerning image
fusion is to determine how to combine the
sensor images.
 Fusion techniques are commonly divided into
two categories:
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
Spatial Domain Techniques:

Transform Domain Techniques :
Many fusion rules have been proposed in the
existing literature, which are categorized, as
follows:
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Fuse by averaging the corresponding coefficients
in each image (‘mean’ rule).
Fuse by selecting the greatest in absolute value
of the corresponding coefficients in each image
(‘max-abs’ rule).
In existing literature Several transforms have already
been used such as DCT, DST, DFT, DWT in fusion
application.
The steps of algorithm based on transform domain
technique are summarised as follow :
(i) Given images, take the transform of these images.
(ii) Obtain the transform coefficients of the images.
(iii) Fuse the images by proper selection rule.
(iv)Take the inverse transform.
(v) Obtain the fused image.
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This paper investigates the effect of use of
different types of masks in discrete cosine
transform (DCT) domain for image fusion
applications.
Here we have used different types of masks
such as rectangular, triangular, strip and fan
shaped mask.
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i.
ii.
iii.
iv.
Masking is used to retain some portion of one
image and some of other image.
Here, I have studied four type of mask, which are
given below........
Rectangular mask
Triangular mask
Fan shaped mask
Strip mask
IMAGE F1
IMAGE F2
TRANSFORM
OF IMAGE
F1
TRANSFORM
OF IMAGE
F2
MASKING
OF IMAGE
T(F1)
COMPLEME
-NTARY
MASKING
OF IMAGE
T(F2)
FUSED
IMAGE
ADDING
MASKED
IMAGE
INVERSE
OF ADDED
IMAGE
The steps of image fusion algorithm in DCT domain
by masking technique are summarised as follow :

Given two images, F1 and F2.

Taking transform(DCT) of the images F1 and F2.
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Apply mask on both transformed images and
added.
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Taking inverse transform of resultant image.

Resultant image is the FUSED image.
Image F1
Image F2
Test Images which are used in this
paper for image fusion
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The acceptable quality for the fused image is set
by the receiver of the image which is usually the
human observer.
Therefore, quality assessments of fused images
are traditionally carried out by visual analysis.
Other than human visual analysis ,we introduce
some statistical measures such as the SNR, PSNR,
and MSE, RMSE which require an ideal or reference
image when applied.
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The quality of a signal is often expressed
quantitatively with the signal-to-noise ratio defined
as:
Where Energy signal is the sum of the squares of the
signal values.
Energy noise is the sum of the squares of the noise
samples.
where z(m,n) is our estimated signal(fused image)
and o(m,n) is original signal.
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The RMSE between the reference image R and the
fused image F is:
where R(i, j) and F(i,j) are the pixel values at the (i, j )
coordinates of the reference image and the fused
image, respectively.
The image size is I ×J .
Out of focus image 1
Rectangular mask
Out of focus image 2
Strip mask
Triangular mask
Reference image
Fan shaped mask
S.N.
TYPE OF MASKING
SNR
SNR(dB)
RMSE
1.
RECTANGULAR SHAPED
MASKING
13.2641
11.22
.09433
2.
TRIANGULAR SHAPED
MASKING
6.6781
8.2465
.24617
3.
FAN SHAPED MASKING
15.0963
11.7887
.08944
4.
STRIP MASKING
14.1944
11.5212
.09219
In this paper, a new method for image fusion
has been proposed using masking technique
in DCT domain.
 The
image fusion results of proposed
method have been compared with some
existing technique.
 The results show that this method can
achieve better quality of fused image using
fan shape mask as compared to other mask.
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[1] Daniel Mueller1, Anthony maeder2, and Peter o’shea1, “The
generalised image fusion toolkit (gift),” Release 4.0, September 13,
(2006) 1queensland university of technology, Brisbane, australia,2ehealth research centre,csiro ict centre, Brisbane, Australia.
[2] Mohamed, M.A. “Implementation of image fusion techniques for
multi-focus images using FPGA” Radio science conference (NRSC).
[3] Anil K. Jain, [Fundamentals of digital image processing],PHI.
[4] Gema Piella Fenoy, “Adaptive wavelets and their applications to
image fusion and compression,” Thesis of the research work that has
been carried out at the centre for mathematics and computer
science (CWI), Amsterdam,(2003).
[5] Tania Stathaki , [Image fusion], Academic press, (2008)
[6] H.B. Mitchell , “Image fusion”. Springer-verlag , Berlin, (2008).
[7] K. K. Sharma and Mohit Sharma, “Image fusion based on image
decomposition using self-fractional Fourier functions”, Signal, image
and video processing (2012), doi: 10.1007/s11760-012-0363-8.







[8] Nikolas Mitianoudis , Tania Stathaki, “Pixel-based and regionbased image fusion schemes using ice bases,”Communications and
signal processing group, imperial college London, exhibition road,
sw7 2az London, uk.
[9] Syed Ali Khayam, “The discrete cosine transform(DCT) theory and
application1,” Department of electrical & computer engineering
michigan state university march 10th (2003)
[10] B.Chanda and D.Dutta Majumdar , [Digital image processing
and analysis], Prentice hall of India, 2000.
[11] Chipman, l. J., and Orr, T. M. “Wavelets and image fusion,” In
proceedings of the IEEE international conference on image
processing (Washington D.C., October 24-26 ,(1995)), pp. 248-251.
[12] Deepa Li a. Godse, Dattatraya S. Bormane, “Wavelet based image
fusion using pixel based maximum selection rule,” Deepali a. Godse
et al. / international journal of engineering science and technology
(IJEST).
[13] Haritha Mallavarpu, Iman Tahvili, Hemanth Verramestti and
Wlodek J. Kulesz, “Implementation of image fusion algorithm using
discrete cosine transform (DCT-iii),” s15_jasmineblommor.
[14] S. G. Mallat, “A theory for multiresolution signal decomposition:
the wavelet representation,” IEEE Transactions on pattern analysis
and machine intelligence 11 (1989), 674-693.
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
[15] S. G. Mallat, [A wavelet tour of signal processing], Academic
press, San Diego, (1998).
[16] G.H. Qu, D. Zhang and P. Yan, “Information measure for
performance of image fusion,” Electronics letters,vol. 38, no. 7,
(2002), pp. 313–315.
[17] C.S. Xydeas and V. Petrovic, “Objective image fusion
performance measure,” Electronics letters, vol. 36, no.4, (2000), pp.
308–309.
[18] Q. Wang, Y. Shen, Y. Zhang and J.Q. Zhang, “A quantitative
method for evaluating the performances of hyper spectral image
fusion,” IEEE Transactions on instrumentation and measurement, vol.
52, no. 4, (2003), pp.1041–1047.
[19] Q. Wang, Y. Shen, y. Zhang and J.Q. Zhang, “Performance
evaluation of image fusion techniques,”
Department of control science & engineering, Harbin institute of
technology, T.R china.
[20] Arash Golibagh Mahyari and Mehran Yazdi, member, IEEE,
“Panchromatic and multispectral image fusion based on
maximization of both spectral and spatial similarities,” IEEE
Transactions on geosciences and remote
sensing, vol. 49, no. 6, June (2011).