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A NOVEL IMAGE FUSION APPROACH USING HIGH RESOLUTION IMAGE
ENHANCEMENT TECHNIQUE
ASWIN KUMER S V
Research Scholar
Department of Electronics and Communication Engineering, SCSVMV University,
Enathur
Kanchipuram, Tamil Nadu-631502, India
Dr.S.K.Srivatsa
Senior Professor (Retd)
Anna University, MIT Chromepet
Chennai, Tamil Nadu-600028, India
Abstract
The term image fusion can be depicted in various path in image, application, techniques
utilized. As a rule, it is characterized as the mix of low resolution MS (Multi Spectral) image
and high resolution PAN (Panchromatic) image for better investigation. The subsequent fused
image will have more data than any source image. The issue recognized here is that, since the
source image is low resolution in nature, negligently the intertwined image will be more
exact than the source image. Subsequently, this paper has added to the post preparing of
image fusion strategies. The upgrade of the subsequent intertwined image will enhance the
nature of the image than the source image and fused image. The proposed image fusion
strategy is produced utilizing Matlab and assess with different test image. The proposed
technique is assessed regarding Peak-Signal-to-Noise-Ratio (PSNR), Mean Square Error
(MSE), Maximum Difference (MD) and Normalized Absolute Error (NAE) and in the whole
situation, the proposed fusion strategy outflanks the ordinary image combination strategy.
Keywords: Image Fusion; Discrete Wavelet Transform (DWT); Resolution.
I. Introduction
Image fusion is to acclimatize the various images of a similar target or scene as indicated by a
specific numerical model and shape, reliable and correct images are more suitable. Image
fusion can be basically relevant to different field of use, for example, restorative imaging,
security, military, out of reach detecting, computerized camera and handheld gadgets. The
fused image contains more data mollified for the scene than any sources image. The
fundamental goal of utilizing fusion is to trim an intertwined result that conveys the most
total and dependable information plausible. Fusing numerous data sources together
additionally creates a more productive outline of the information [1].Data fusion can be
performed at any level of the image data portrayal. Predictable to different types of
International Journal of Pure and Applied MathematicsVolume 116 No. 23 2017, 671-683ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu
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information fusion, image fusion is normally performed at one of the three diverse handling
levels to be specific Pixel, Feature and Decision Level [2].
Principal component analysis (PCA) is a prominent plan for highlight reflection and
estimation decay and is utilized for image fusion. Image fusion is a technique to create a
solitary high helpful image from one or included information images. Pixel level, feature
level, signal level and decision level are different stage at which image fusion can be helpful
for some application [3].A number of image fusion strategies have been shown in the fiction.
Furthermore of unobtrusive pixel level image fusion systems, we found the intricate
procedures, for example, Laplacian Pyramid [4], Morphological pyramid [5], fusion in light
of PCA [6], Discrete Wavelet Transform (DWT) [7].During the fusion strategy, all the vital
optical data found in the information images must be moved into the intertwined image
without diagram of items. In amassing, the fusion system ought to be dependable and robust
to blemishes such as noise.
2. Related Work
Motivation for image fusion is for the most part the progression toward late specialized
advances in the fields of image processing execution strategy. Enhanced quality and
expanded reason for current imaging sensors openness at a lower charge have made the
utilization of different gadgets basic in a scope of imaging uses [8].
Image fusion methodology can be described into three phases. They are specified to as pixel,
article and decision level of delineation temporary on the level at which fusion happen [9].
These fusion gadgets can be generally portrayed into spatial domain and transform domain
fusion.Brovey technique, Principal Component analysis (PCA), IHS (intensity hue saturation)
and High pass filtering strategies falls under the spatial domain. Spatial image fusion is
handled by blending the pixel estimations of the two source or supplementary images.
2.1 Principal Component Analysis (PCA)
Principal component analysis (PCA) is a huge math handle that believers multivariate data
with associated factors into one with uncorrelated factors [10] and this system is connected to
the Multi Spectral (MS) bands. The PCA transforms bury associated MS bands into another
arrangement of uncorrelated mechanisms. The primary component is exchanged by a high-
resolution PAN (i.e. Panchromatic Image) for the combination. The reverse PCA transform is
finished to bring melded dataset once more into the inventive multispectral include space.
X1
Principal Component
Analysis
X1Y1 + X2Y2
Y1
Y2
Fused
Image
X2
Xf
Listed Basis
Image
Figure 1. Image Fusion using PCA [11]
The Procedure involved in the PCA based image fusion;
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a. Input images estimate testing is finished to affirm that source images are of equivalent
size.
b. Then input images are chosen into segment vectors. Let Z is the subsequent section
vector of estimation 2*N.
c. Compute the test mean along every section. The estimations of exploratory mean vector
Ev is 1*2.
d. Subtract Ev from every segment of framework Z. The resulting framework X has
measurement 2*N.
e. Compute covariance framework C of lattice X.
f. Calculate the Eigen vector and Eigen estimation of C and sort them in lessening
request.
g. Study first segment of vector which relate to more noteworthy Eigen incentive to figure
regularized module X1 and X2.
2.2 Discrete Cosine Transform (DCT)
Discrete Cosine Transformation (DCT) is vital to various applications in science, designing
and in image compression like MPEG and so on. For effortlessness, Discrete Cosine
Transformation (DCT) can change over the three-dimensional space image to presence area
image. The procedure of image fusion in light of Discrete Cosine Transform (DCT) is
appeared in Figure 2. Vast DCT amounts are gathered in the little recurrence locales, thus, it
is known to have great vitality conservativeness.
Fusion
Rule
IDCT
DCT
DCT
First
Image
Second
Image
Fused
Image
Figure 2. Image fusion processing using DCT [11]
The meaning of the two-dimensional DCT for an info image A and yield image B is
𝐵𝑥𝑦 = 𝛼𝑥𝛼𝑦 𝐴𝑝𝑞𝐶𝑂𝑆𝜋 2𝑝+1 𝑥
2𝑃
𝑄−1𝑞=0 𝐶𝑂𝑆
𝜋 2𝑞+1 𝑦
2𝑄,𝑃−1
𝑝=0 0 ≤ x ≤ P − 1 0 ≤ y ≤ Q − 1 (1)
Where, 𝛼𝑥 =
1
𝑃 , 𝑥 = 0
2
𝑃, 1 ≤ 𝑥 ≤ 𝑃 − 1
Where, 𝛼𝑦 =
1
𝑄 , 𝑦 = 0
2
𝑄, 1 ≤ 𝑦 ≤ 𝑄 − 1
Here "P" and "Q" are the line and segment extent of "A" separately. On the off chance that
you spread on the DCT to real information, the result is additionally genuine. The DCT tends
International Journal of Pure and Applied Mathematics Special Issue
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to focus information, which is helpful for image handling introductions. There are a few
conditions in an image handling where high spatial and high spectral resolution in one image
is fundamental. The primary commitment of image fusion is expansion of the grey level high
resolution panchromatic image and the beautified low resolution multispectral image.
3. Proposed Algorithm
In existing methods, the fused image is assessed regarding PSNR (Peak-Signal-to-Noise-
Ratio), MSE (Mean Square Error) and contrasted and both source images. The motivation of
this paper is that, we officially mindful that, one of the source images has low resolution;
neglectfully the fused image will be better when contrasted with the source image. Likewise
we understood that there is couple of commitments have been proposed to enhance the fused
image, which is in the post handling of the fused image.
3.1 Proposed SVD Techniques
The factorization of rectangular genuine or multifaceted grid into transverse symmetric or
Hermitian square lattices utilizing Eigen vectors is the fundamental source utilized as a part
of straight polynomial math for SVD. In charge to disperse the framework into an
arrangement of straightly freed constituents with these parts having their own vitality
constituents, SVD is the steady and operative system. SVD representation of digital image X
with size MxN is as per the following,
[𝑋] 𝑁
𝑀= [𝑈]
𝑀
[𝑆] 𝑁
𝑀[𝑋]
𝑇𝑁
(2)
U= [u1, u2, … um]
V= [v1, v2,...vn]
Where S=Singular value diagonal framework, U is a MxM orthogonal framework, V is a
NxN orthogonal grid, and S is a MxN lattice with the corner to corner starting points speaks
to the solitary esteems, si of X. Singular value (SV) indicates the luminance of an image layer
and the relating pair of singular vectors (SCs) connotes the geometry of the image.
Image Capacity Gray Scale
Adaptation
Spherical
Averaging Filter
Load Position
Image
SVD
Execution
Image
Modernization
Adaptation of
Image to dual
datatype
Figure 3. Block diagram representation of SVD computer algorithm
Subsequent to relating the SVD image fusion on the fractional image, material substance of
the image were composed. Root mean square error (RMSE) parallels to pixels in the
reference error Ir and the fused image If. On the off chance that the reference image and
fused image are comparable then the RMSE esteem proportional to zero and it will increment
when the distinction ascends between the reference and intertwined image. Figure 3
represents to the piece outline of steps involved in SVD.
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RMSE = 1
𝑝∗𝑞 [𝑅 𝑖, 𝑗 − 𝐹 𝑖, 𝑗 ]˄2𝑞
𝑗 =1𝑝𝑖=1 (3)
Peak signal to noise ratio (PSNR) esteem will be enhanced when the fused and reference
images are comparable and more noteworthy esteem infers unrivaled fusion. PSNR is
registered by following formula.
PSNR = 20 log 10 L˄2
1
p∗q (R i,j −F i,j )˄2q
j−1pi−1
(4)
3.2 Mean Square Error
Mean Square Error can be unsurprising in one of numerous approaches to gauge the
fluctuation between qualities camouflaged by an assessment and the exact quality for
execution assessment. MSE is a hazard work steady to the gauge the estimation of balanced
error.
MSE= 1 XY [f(𝑥 𝑦 )𝑦𝑦=1
𝑥𝑥=1 − f ,(𝑥 𝑦 )] (5)
The MSE is equivalent to the sum of variance and square of bias of estimator.
MSE (𝜃 ) = Var. (λ) + (Bias (f , f) 2) (6)
The flowchart calculation of the image enhancement erosion and acquisition techniques
represents in Figure 4 calculates the MSE and PSNR techniques.
Image Acquisition
Image Enhancement
Using Erosion
Calculation of MSE &
PSNR of Different
Enhancement Techniques
Figure 4. Flow chart for calculation of PSNR &MSE in an image
3.3 Neural Network Algorithm
The partitioning of the M*N squares has the extraction of the standardized image in fusion
strategies. The images has changed over to fused image to the M*N diagonal Matrix.
The calculation initially deteriorates the source images into squares. Given two of these
pieces (one from each source image), a neural system is prepared to finish up which one is
clearer. Fusion then continues by choosing the clearer obstruct in developing the last image.
In Discrete Wavelet Transform (DWT) based image fusion the shift variation will command
the execution of the fusion technique. The utilization of image pieces then again, keeps away
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from this issue regardless of the possibility that there is question development or mis-
enrollment in the source images. In stepwise working of the actualized technique is given in
detail,
1) LFi is the left engaged and RFi is the privilege centered forms of the ith image in the
dataset.
2) Divide the adaptations LFi and RFi of each image in the dataset into "k" number pieces of
the size M*N.
3) Create the components petition for all LFij and RFij as per the elements indicated. Here
j=1,2,3...k. For all 'i', there are two arrangements of components esteems for each square "j"
named as FSLFij and FSRFij each of which contains five element esteems. Subtract the
elements estimations of square "j" of RFi and incorporate this example in highlight
document. Standardize the component esteem between [0 1].
4) Assign the class an incentive to each piece "j" of ith picture. On the off chance that piece
"j" is noticeable in LFi then allot it class esteem 1 generally give it a class esteem - 1. If there
should arise an occurrence of class esteem - 1, square "j" is noticeable in RFi.
5) Train a neural system to decide if LFi or RFi is clearer. Recognize the clearness of the
considerable number of squares of any match multi-focus images to be intertwined.
6) Fuse the given combine of multi-focus images obstruct by square as per the
characterization consequences of the neural system. with the end goal that yield of NN for
piece "j" If>0, select "j" from left-engaged picture If<0, select "j" from right-engaged Image
The square graph of the actualized technique is appeared in Figure 5.
. .
Create left and
right focused
version
LFRB
LBRF
Divide into
M*N Blocks
Extract
features and
Normalize
Generate
fused
Image
High Quality
Enhanced
Image
Image 1
Image 2
Image 3
Figure 5. Block Implementing Method
The processing technique of normal image to fused image is represented by Figure 6. It
explains the quality of the source image when compared with the resulting fused image.
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Figure 6. Normal Image to Fused Image Technique
Figure 7. High Resolution Fused Image Enhancement
The proposed preparing system the subsequent fused image is upgraded. The upgrade method
signified in this paper is to enhance the quality and determination of the intertwined image.
Typical fused image when contrasted with proposed strategy indicates better outcomes as in
Figure 7.
4. Performance Analysis
The outcomes in type of different parameters are appeared in the tables underneath with the
relating diagrams. Table 1 and Figure 8 contrast the proposed and existing strategy and
regard to the mean square error (MSE) individually.
Figure 8. Design of MSE readings for 12 image sets
Table 1. Mean Square Error (MSE) Evaluation
Image Name Conventional
Image Fusion Proposed Method
Normal Image Fused Image
Normal Image Enhancement Image High Resolution
Fused Image
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Method
Image 1 1.281 0.0713
Image 2 7.026 3.8113
Image 3 6.126 3.8759
Image 4 3.681 3.923
Image 5 33.214 8.537
Image 6 38.121 3.0177
Image 7 3.7622 3.6213
Image 8 11.127 3.8721
Image 9 4.2621 3.8788
Image 10 16.012 3.776
Image 11 42.213 40.0321
Image 12 53.7102 51.0712
Bigger PSNR demonstrates a littler contrast between the first and the reproduced image. In
Figure 9 and Table 2 it is demonstrated that the PSNR estimation of proposed calculation is
substantially more noteworthy than that of the customary calculation. Accordingly the
proposed calculation is giving better outcomes as far as PSNR.
Figure 9. Design of PSNR readings for 12 image sets
Vast estimation of MD implies that picture is of low quality. In Figure 10 and Table 3 the
greatest contrast estimations of proposed calculation is lower than the MD estimations of the
current technique. Consequently our proposed calculation gives better outcomes.
Table 2. Peak Signal to Noise Ratio (PSNR) Evaluation
Image Name
Conventional
Image Fusion
Method
Proposed
Method
Image 1 45.7123 59.1531
Image 2 38.1632 43.0121
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Image 3 40.0731 42.1479
Image 4 41.1207 42.1209
Image 5 31.7588 38.8197
Image 6 31.6311 43.2913
Image 7 41.1232 41.1232
Image 8 36.4201 42.1412
Image 9 41.4031 42.1269
Image 10 36.0102 42.3329
Image 11 31.6132 32.1926
Image 12 30.7126 31.0817
Figure 10. Design of MD readings for 12 image sets
Table 3. Maximum Difference (MD) Evaluation
Image Name
Conventional
Image Fusion
Method
Proposed
Method
Image 1 36 36
Image 2 21 9
Image 3 10 4
Image 4 4 4
Image 5 17 3
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Image 6 24 7
Image 7 4 4
Image 8 11 3
Image 9 11 5
Image 10 35 7
Image 11 93 91
Image 12 111 102
Bigger estimation of NAE (Normalized Absolute Error) demonstrates low quality of the
picture. In Figure 11 and Table 4 the NAE estimations of proposed image fusion technique
are not exactly the NAE estimations of the regular image fusion strategy. In this way the
proposed strategy enhanced as far as NAE.
Figure 11. Design of NAE readings for 12 image sets
Table 4.Normalized Absolute Error (NAE) Evaluation
Image Name
Conventional
Image Fusion
Method
Proposed
Method
Image 1 0.0077 0.0006701
Image 2 0.0121 0.0109
Image 3 0.0165 0.0135
Image 4 0.0162 0.0162
Image 5 0.0223 0.0163
Image 6 0.0243 0.0083
Image 7 0.0166 0.0166
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Image 8 0.0212 0.0137
Image 9 0.0191 0.0202
Image 10 0.0221 0.0132
Image 11 0.0117 0.0201
Image 12 0.0499 0.0407
V. Conclusion
Image fusion is the way toward joining information of twofold or additional images into a
lone image which can hold extremely critical scenes of the every single unique image. It is
discovered that DCT based image fusion shape comes about, however with less lucidity, less
PSNR esteem and high Mean square error. It is discovered that the vast majority of
collaborators have ignored image sifting and rebuilding which is must need of the image
fusion. So that the proposed work utilizing SVD systems has better outcomes to analyze the
ordinary technique (i.e. DCT methods). Image fusion in view of wavelet domain
transformation indicates promising outcomes, in this way in work it is used. After this fusion
procedure, the image enhancement method is utilized, with the goal that we can demonstrate
that the outcomes are more precise than any of the source images and fused images.
Though, in the current work source image has the low resolution, carelessly the intertwined
image will be better. The proposed work of upgrade strategies needs to enhance the fused
image which is post handling of the fused image. After the fusion procedure, image
enhancement strategies has utilized. This proposed systems brings about more exact and high
resolution image than the source and fused image.
References
1. Min Xu; Hao Chen; Varshney, P.K. (2011). An Image Fusion Approach Based on
Markov Random Fields, Geoscience and Remote Sensing, IEEE Transactions on, 49(12),
5116-5127.
2. Gonzalo Pajares and Jesus Manuel de la Cruz, (2004). A wavelet-based Image Fusion
Tutorial, in pattern Recognition, 37(9), 1855-1872.
3. H. Nasir, V. Stankovic, S. Marshall, (2011). Singular value decomposition based fusion
for super resolution image reconstruction, in Proc. IEEE International Conference on
Signal and Image Processing Applications (ICSIPA).
4. A.Toet, (1989). Image fusion by a ratio of low pass pyramid, in pattern Recognition
Letters, 9(4), 245-253.
5. Yufeng Zheng, Edward A. Essock and BruceC.Hansen, (2004). An Advanced Image
Fusion Algorithm Based on Wavelet Transform-Incorporation with PCA and
morphological processing, in Processing of the SPIE, 52(98), 177-187.
6. V.P.S.Naidu and J.R.Raol, (2008). Pixel-level Image Fusion using Wavelets and
Principal Component Analysis, in Defence Science Journal, 58(3), 338-352.
7. H.Li, S.Manjunath and S.K.Mitra, (1995). Multi-sensor image fusion using the wavelet
transform, in Graphical Models and Image processing, 57(3), 235-245.
8. Anjali Malviya, S.G.Bhirud, (2009). Image fusion of digital image, International journal
of recent trends in engineering, 2(3).
9. Patil, Ujwala, and Uma Mudengudi, (2011). Image fusion using hierarchical PCA, IEEE
International Conference on Image Information Processing (ICIIP), 1-6.
International Journal of Pure and Applied Mathematics Special Issue
681
10. Mohamed, M. A., and B. M. El-Den, (2011). Implementation of image fusion techniques
for multi-focus images using FPGA, IEEE 28th National Radio Science Conference
(NRSC), Cairo, 1-11.
11. Desale, Rajenda Pandit, and Sarita V. Verma, (2013). Study and analysis of PCA, DCT
& DWT based image fusion techniques, IEEE International Conference on Signal
Processing Image Processing & Pattern Recognition (ICSIPR), Coimbatore, 66-69.
12. Rajesh, M. & Gnanasekar, J.M. Wireless Pers Commun (2017).
https://doi.org/10.1007/s11277-017-4565-9
13. Rajesh, M., and J. M. Gnanasekar. "GCCover Heterogeneous Wireless Ad hoc
Networks." Journal of Chemical and Pharmaceutical Sciences (2015): 195-200.
14. Rajesh, M., and J. M. Gnanasekar. "CONGESTION CONTROL IN
HETEROGENEOUS WANET USING FRCC." Journal of Chemical and Pharmaceutical
Sciences ISSN 974: 2115.
15. Rajesh, M., and J. M. Gnanasekar. "Consistently neighbor detection for MANET."
Communication and Electronics Systems (ICCES), International Conference on. IEEE,
2016.
16. Rajesh, M., and J. M. Gnanasekar. "Hop-by-hop Channel-Alert Routing to Congestion
Control in Wireless Sensor Networks." Control Theory and Informatics 5.4 (2015): 1-11
17. Rajesh, M., and J. M. Gnanasekar. "Annoyed Realm Outlook Taxonomy Using Twin
Transfer Learning" International Journal of Pure and Applied Mathematics, 116.21
(2017) 547-558.
18. Rajesh, M., and J. M. Gnanasekar. "Get-Up-And-Go Efficientmemetic Algorithm Based
Amalgam Routing Protocol." International Journal of Pure and Applied Mathematics,
116.21 (2017)537-547.
19. Rajesh, M., and J. M. Gnanasekar. "Congestion Control Scheme for Heterogeneous
Wireless Ad Hoc Networks Using Self-Adjust Hybrid Model." International Journal of
Pure and Applied Mathematics, 116.21 (2017)537-547.
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