fusion technique formulti-focus images using nsc...

ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)

Volume 6, Issue 7, July 2017

815 All Rights Reserved © 2017 IJARECE

Fusion technique forMulti-Focus Images

using NSC Transform 1G.SESHASAI,

2K.SRAVAN KUMAR

1PG SCHOLAR, ELECTRONICS AND COMMUNICATION ENGINEERING, JNTUA COLLEGE OF

ENGINEERING, ANANTAPUR, AP, INDIA, INDIA

2 ADHOC LECTURER, ELECTRONICS AND COMMUNICATION ENGINEERING, JNTUA COLLEGE OF

ENGINEERING, ANANTAPUR, AP, INDIA, INDIA

Abstract-In multi-scale transform domain based image

fusion the selection of sub-band coefficients is difficult

to solve this problem,this paper presents a novel hybrid

multi-focus image fusion method. First, decompose the

source multi-focus images using the non-subsampled

contourlet transform (NSCT). The process of fusion

undergoes in two stages namely initial fusion and final

fusion. The initial fusion contains, a low frequency sub

band coefficients are fused by sum-modified-laplacian

(SML) based local visual contrast, while the high

frequency sub band coefficients fused by the local log-

Gabor energy. The initial fused image is subsequently

reconstructed based on the inverse NSCT with the fused

coefficients. The existing methods are transform-based

fusion methods, discrete wavelet transform, and spatial

based method.

Based on the result of initial fusion,

morphological opening and closing are employed for

post-processing to generate a fusion decision diagram.

By this diagram pixels of the source image and the

initial fusion image are selected to obtain the final

fusion image. The proposed method can provide a

better performance than the existing fusion methods

whatever the source images are clean or noisy.

Index Terms-Multifocus image fusion, non-subsampled

contourlet transform (NSCT), Log-Gabor energy and

mathematical morphology.

I.INTRODUCTION

Now a days, the image fusion is having a great

importance in image processing systems.The purpose of

image fusion is to combine different images from several

sensors orthesamesensoratdifferent times to create a new

image that will be more accurate and comprehensiveand

then it ismore suitable for a human

administratororotherimageprocessingtasks[3].Currently,i

mage fusiontechnology hasbeenusedindigitalimaging,

remote sensing,biomedicalimaging,computervision, and

soon[4]–[6].

Indigitalcameras,opticalmicroscopes or other

equipment’s havelimiteddepth-of-focusof opticallens,

soimpossible tocapturetotalimagethat containsall

relevantfocusedobjects. Someobjects areinfocus with

clear,butother objects are far

fromlenswillbeoutoffocusand, thus,blurred[8].However,

inreality,peopletendtoobtaina

clearimageofalltargets.Abestwaytoovercome this

problemistomovemulti-focusimagefusiontechniques, in

whichonecanobtainoneimagewithalloftheobjectsinfocus

bywayofitcontainingthebestinformation

frommoreoriginalimages[9].

Image fusion methods are two types, spatial

domainandtransformdomainfusiontechniques [10].Fusion

methods in the transform domain

basedmethods,eachsourceimageisfirst decomposed intoa

sequence ofimagesthroughaparticular mathematical

transformation.Then,thefusedcoefficients

areobtainedthrough

somefusionrulesforcombination.Finally,thefusionimage

isobtainedbymeansofamathematical inversetransform.

Thus,thetransformdomainfusion methodsarealsoknownas

Multi-scalefusionmethods.

II.PRELIMINARIES

This section provides the related concepts of NSCTon which the proposed framework is based and NSCT for image fusion.

Non-Subsampled Contourlet Transform

Fig 1.Non subsampled contourlet decomposed schematic

diagram




The NSCT inherits from CT and it will have some

advantages Enhances directional selectivity and shift

invariance.NSCTis based on Non subsampled Pyramid Filter Banks (NSPFB) and Non subsampled Directional Filter Banks (NSDFB). Fig. 1 gives the non-subsampled contourlet decomposition framework with k= 2 levels.

By this NSPFB it will produce one low frequency sub image and one high frequency sub image at each decomposition level and it is multiscale and two channel non subsampled filtered bank. The subsequent

decomposition levels of Non-subsampled Pyramid (NSP) are carried out to decompose the low frequency component available iteratively to capture the line or plane singularities in the image. As a result, NSP can obtain k+1 sub-images, including one low and k high frequency sub-images. These

sub-images have the same size as the source images.The

NSDFB used for directional decomposition in each

high frequency sub images. NSDFB is two-channel non-

subsampled filter banks constructed by eliminating the down-samplers and up-samplers and combining the directional fan filter banks in the DFB. This directional sub images and source images size are same.NSDFB provides the NSCT the multi direction performance and offers more precise directional detail information to obtain more

accurate results. Therefore, NSCT is more suitable for image fusion.

Fig 2.Schematic diagram of NSCT image fusion

III. INITIAL FUSED IMAGE BASED ON NSCT

This section provides the low and high frequency fusion rules in NSCT. In fusion processa low frequency sub band coefficients are fused by sum-modified-laplacian (SML) based local visual contrast, while the high frequency sub band coefficients fused by the local log-Gabor energy. A. Fusion of Low frequency subbands

The low frequency subbands contains the

approximate information of source image, it reflect gray component of source image and most energy of source image. For this coefficients we use SML based local visual contrast.

SML based local visual contrast =

𝑆𝑀𝐿 (𝑖,𝑗)

𝐿^(𝑖 ,𝑗)1+∝ , 𝑖𝑓 𝐿𝑖 ,𝑗 ≠ 0

𝑆𝑀𝐿 𝑖, 𝑗 , 𝑜𝑡𝑕𝑒𝑟𝑤𝑖𝑠𝑒

Where α ϵ (0.6, 0.7) is a visual constant

SML(i,j)= [𝑀𝐿(𝑖 + 𝑚, 𝑗 + 𝑛)]𝑁𝑛=−𝑁

𝑀𝑚=−𝑀

Where ML(i,j) is modified laplacian with (2M+1)(2N+1) window size

ML(i,j)=|2L(i,j)-L(i-step,j)-L(i+step,j)|+|2L(i,j)-L(i,j-step)-L(i,j+step)|

Step is a variable spacing between coefficients and always is

equal to 1. L(i,j) is coefficient located at (i,j) in low frequency subbands.

This fusion rule not only consider the nonlinear relationship between the contrast sensitivity threshold of HVS and the background luminance, it consider single pixel to measure local contrast of low frequency coefficients so these fusion rule have great performance.

B. Fusion of High Frequency Subbands

The high frequency components represents the edges, textures and boundaries so on. High frequency fusion rule based on local Log-Gabor energy. It may have consists of Log-Gabor filter this filter is most prominent for edges and textures Log-Gabor filter defines as

g(f,θ)=exp{-[ln(𝑓/𝑓0)]2

2[ln(𝜎/𝑓0)]2} ∗exp {-

(𝜃−𝜃0)2

2𝜎𝜃 2}

wheref0 is center frequency of Log-Gabor filters

θo is direction of filter σ – Bandwidth, Bf of radial filter σθ – Bandwidth , Bθ of orientation

Bf = 2

𝑙𝑛2

2*|ln(𝜎/𝑓0)| , Bθ =2σθ 2𝑙𝑛2

This is Log-Gabor filter. Log Gabor energy at local area around the pixel (i,j) =

𝐷𝐿𝐺𝐸𝑘𝑙 𝑖, 𝑗 =

1

(2𝑀 + 1)(2𝑁 + 1) 𝐿𝐺𝐸𝑘𝑙(𝑖 + 𝑚, 𝑗 + 𝑛)

𝑁

𝑛=−𝑁

𝑀

𝑚=−𝑀

Where (2M+1)(2N+1) is window size. Log Gabor energy of high frequency subbands at Kth scale Lth direction-

LGEkl(i,j) =

𝑟𝑒𝑎𝑙(𝐺𝑘𝑙𝑈𝑉 𝑖, 𝑗 2 + 𝑖𝑚𝑎𝑔(𝐺𝑘𝑙

𝑈𝑉(𝑖, 𝑗)2)

𝑉

𝑉=1

𝑈

𝑈=1

Where 𝐺𝑘𝑙𝑈𝑉(i,j) – log Gabor wavelets in scale U and V

direction.

IV.FOCUSED AREA DETECTION AND PROPOSED FRAME

WORK A. Focused Area Detection

Decomposition and reconstruction of image may cause the lose in source image information so we move to focused area detection and prevent the loss which is happen in the initial fusion process. Comparing the source image with initial fused image, it is

easy to find that pixel in the focused are have similarity to corresponding pixels of initial fused image. It is from Root Mean Square Error (RMSE) means difference of two images.

𝑅𝑀𝑆𝐸𝑥(𝐼, 𝐽)= [𝑓𝐹 𝑖+𝑚 ,𝑗+𝑛 −𝑓𝑥 𝑖+𝑚 ,𝑗 +𝑛 ]²𝑁

𝑛=−𝑁𝑀𝑚 =−𝑀

(2𝑀+1)(2𝑁+1)

Where (2M+1)(2N+1) is window size




fF-fused image fx– source image After the focused area detection to remove the defects mathematical morphology methods are used.

B.Proposed Frame Work

Fig 3. Schematic diagram of proposed image fusion.

V.THE EXPERIMENTAL RESULTS

In this section we will get the results. Generally for good image fusion process are: 1. It should be able to extract the complementarily information from source images. 2. It should be robust & reliable. 3. It must not introduce inconsistencies or artifacts according to HVS. Objective measure of image fusion: 1. Mutual Information : mutual information between source and fused images.

MI = MIAF + MIBF In which

MIAF

= 𝑝𝐿𝑎=0

𝐿𝑓=0

AF(a,f)log2 (

𝑝𝐴𝐹 𝑎 ,𝑓

𝑝𝐴 𝑎 𝑝𝐹 𝑓 )

MIBF

= 𝑝𝐿𝑏=0

𝐿𝑓=0

BF(b,f)log2 (

𝑝𝐵𝐹 𝑏 ,𝑓

𝑝𝐵 𝑏 𝑝𝐹 𝑓 )

Where MIAF& MIBF – Normalized MI between fused and source images a and b f Є[0,L]

PA(a), PB(b) and PF(f) – Normalized gray level histograms of source and fused image. PAF(a,f) , PBF(b,f) –joint gray level histogram between fused image and source images A and B Greater the value of MI better fusion.

2. Edge Based Similarity Measure : Similarity between the edges transferred from input to fused image.

𝑄𝐴𝐵/𝐹= [𝑄𝐴𝐹 (𝑖 ,𝑗 )

𝑛𝑜𝑗=1 𝑊𝐴 𝑖,𝑗 +𝑄𝐵𝐹 𝑖 ,𝑗

𝑚𝑜𝑖=1 𝑊𝐵 (𝑖 ,𝑗 )

𝑊𝐴 𝑖,𝑗 +𝑛𝑜𝑗=1

𝑊𝐵 (𝑖,𝑗 )𝑚𝑜𝑖=1

Where mo , no – Size of images WA(i,j) , WB(i,j) – Gradient strength of source images

WA(i,j)= |SiA(i, j) + SjA(i, j)|2

WB(i,j)= SiB I, j + SjB I, j 2

QAF(i,j), QBF(i,j) – Orientation preservation value & edge strength value at location (i,j)

QAF(i,j) = QaAF(i,j)Qg

AF(i,j) QBF(i,j) = Qa

BF(i,j)QgBF(i,j)

Greater the value of edge based similarity measure better fusion.

TABLE I

Comparison on Objective Criteria of Different Methods

Evaluation DWT NSCT

MI (Mutual Information)

6.3933

7.8342

𝑄𝐴𝐵

𝐹 (Edge Based Similarity

Measure)

0.6979

0.7455




SIMULATION RESULTS

Fig 4.Input Source Images

Fig 5.Initial Fused Image

Fig 6. Initial map and Decision map




Fig 7. Final Fused Image

Fig 8. Objective criteria of Different methods




VI. CONCLUSION AND FUTURE SCOPE

Inthispaper,anovelimagefusionschemethatisbased

onNSCTandfocusedareadetectionisproposed formulti

focusimage fusion. Theadvantages of the proposedmethod

include: (1)NSCTismoresuitableforimage

fusionbecauseofsuperioritiessuch asmulti-resolution,multi-

direction,andshift-invariance;(2)usingthedetectedfocused

areasasafusiondecisionmaptoguidethefusionprocess

notonlyreducesthecomplexityoftheprocedure butalso

increasesthereliabilityandrobustness ofthefusionresults;

and(3)theproposed fusionschemecanprevent artifactsand

erroneousresultsattheboundaryofthefocusedareasthatmay

beintroducedbydetectionfocusedareabased methodsduring

thefusion process. Theexperimentalresultsonseveralgroups

ofmultifocusimages,regardlessofwhetherthereisnoise

ornot,haveshownthesuperiorperformance oftheproposed

fusionscheme.TheNSCTalgorithmistime consumingand

ofhighcomplexity,sothenextstepthatwillbestudiedis

howtoimprovethespeedofthealgorithm.

REFERENCES

[1]Yong Yang,Member IEEE,Song Tong,Shuying Huang, and Pan Lin,”Multifocus image fusion based on NSCT and focused area detection,” IEEE Sensors J.,vol. 15,no.5, May 2015.

[2] Y.JiangandM.Wang, “Imagefusionwithmorphologicalcomponent analysis,”Inf.Fusion,vol.18,no.1,pp.107–118,Jul.2014.

[3] S.LiandB.Yang,“Hybridmultiresolutionmethod formultisensormul-timodalimagefusion,”IEEE SensorsJ.,vol.10,no.9,pp.1519–1526, Sep.2010.

[4]S.Chen,R.Zhang,H.Su,J.Tian,andJ.Xia,“SARandmultispectralimagefusion usinggeneralizedIHStransform basedonà trouswavelet andEMDdecompositions,”IEEESensorsJ., vol.10,no. 3,pp.737–745, Mar.2010.

[5] B.Miles,I.B.Ayed,M.W.K.Law,G.Garvin,A.Fenster,andS.Li,“Spineimage fusionviagraphcuts,”IEEETrans.Biomed.Eng.,vol.60, no.7,pp.1841–1850,Jul.2013.

[6] J.Liang,Y.He,D.Liu,andX.Zeng,“Imagefusionusinghigherorder singularvaluedecomposition,”IEEETrans.ImageProcess.,vol.21, no.5,pp.2898–2909,May2012.

[7] B.Yang andS. Li, “Multi-focus image fusion usingwatershedtransform andmorphologicalwaveletclaritymeasure,”Int.J.InnovativeComput.Inf.Control.,vol.7,no.5A,pp.2503–2514,May2011.

[8] B.Yang andS. Li,“Multifocusimagefusionandrestorationwithsparse representation,”IEEETrans.Instrum.Meas.,vol.59,no. 4,pp.884–892, Apr.2010.

[9] W.WangandF.Chang,“Amulti-focusimagefusionmethodbased on Laplacian pyramid,” J. Comput.,vol. 6, no. 12, pp. 2559–2566, Dec.2011.

[10] N. Mitianoudis and T. Stathaki, “Optimal contrast correction for ICA-

basedfusionofmultimodalimages,”IEEESensors J.,vol.8,no.12, pp.2016–2026,Dec.2008.

Mr. G.Seshasaireceived B.Tech Degree in (ECE) From Kottam College of Engineering, Kurnool, in 2014. He is currently doing M.Tech in JNTUA College of Engineering, Anantapur. His areas of interests are Image Processing and Communication.

Mr.K.Sravan Kumar completed B.E in ECE from Magna College of Engineering, Chennai in 2005, and

M.Tech from SRM University, Chennai in 2007.He is currently working as a Lecturer, ECE Department, JNTUA College of Engineering, Ananthapuramu. His areas of interests are Wireless

Communications, SignalProcessing and Cognitive Radio.

fusion technique formulti-focus images using nsc...

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