International Journal of Pure and Applied Mathematics ... · Figure 2 . Image fusion processing using DCT [11] The meaning of the two -dimensional DCT for an info image A and yield

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

[email protected]

Dr.S.K.Srivatsa

Senior Professor (Retd)

Anna University, MIT Chromepet

Chennai, Tamil Nadu-600028, India

[email protected]

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

671

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;

International Journal of Pure and Applied Mathematics Special Issue

672

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


673

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.


674

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


675

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.


676

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


677

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


678

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


679

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


680

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.


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.


682

683

684

Documents

International Journal of Pure and Applied Mathematics ... · Figure 2 . Image fusion processing using DCT [11] The meaning of the two -dimensional DCT for an info image A and yield