research article visibility enhancement of scene...

Research ArticleVisibility Enhancement of Scene Images Degraded byFoggy Weather Conditions with Deep Neural Networks

Farhan Hussain and Jechang Jeong

Department of Electronics amp Computer Engineering Hanyang University Seoul 133-791 Republic of Korea

Correspondence should be addressed to Jechang Jeong jjeonghanyangackr

Received 19 March 2015 Revised 16 May 2015 Accepted 18 May 2015

Academic Editor Wei Wu

Copyright copy 2016 F Hussain and J Jeong This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Nowadays many camera-based advanced driver assistance systems (ADAS) have been introduced to assist the drivers and ensuretheir safety under various driving conditions One of the problems faced by drivers is the faded scene visibility and lower contrastwhile driving in foggy conditions In this paper we present a novel approach to provide a solution to this problem by employingdeep neural networks We assume that the fog in an image can be mathematically modeled by an unknown complex functionand we utilize the deep neural network to approximate the corresponding mathematical model for the fog The advantages of ourtechnique are as follows (i) its real-time operation and (ii) being based on minimal input that is a single image and exhibitingrobustnessgeneralization for various unseen image data Experiments carried out on various synthetic images indicate that ourproposed technique has the abilities to approximate the corresponding fog function reasonably and remove it for better visibilityand safety

1 Introduction

Degraded visibility and lack of luminance in foggy weatherpose a serious threat to the safety of driversThese conditionsincrease the danger of vehicle collision and are a majorcause of injuries and fatalities on roads covered with fogSuspension of very fine droplets in the fog causes blockingand scattering of the light This leads to less light reachingthe driverrsquos eye lower contrast and hence reduced visibility(Figure 1) To enhance visibility in bad weather is an areaof high interest for the researchers Various studies havebeen carried out to observe and model the effects of widerange of weather conditions on vision systems The authorsin [1ndash4] present such weather models They analyze howscenes are affected by various weather conditions Thesestudies attempt to restore various attributes of a scene inan image degraded by bad weather conditions with the helpof these weather models A few researches for example[5 6] suggest the use of polarizing filters to remove theeffect of haze from the images To realize the techniquesdescribed in [5 6] two or more independent images arerequired to comply with the condition that the air light

produces some measurable partial polarization in themWeather removal algorithms proposed in [7] make use of asingle image and some supplementary information providedby the user A deep photo system is proposed in [8] based onthe georegistration of the photographs This georegistrationenables access to large amount of geographic informationsystem (GIS) data such as 3D models for cities buildingsterrains and structures texture models and depth mapsThe authors of this study suggest the augmentation of thisinformation with simple photographs to achieve variousoperations like dehazing relighting view synthesis andexpanding the field of view Defoggingdehazing solutionsrelying entirely on single images are proposed in [9ndash12] Thesingle image visibility enhancement method in [9] developsan optimized cost function based on certain observations ofimages with and without weather effects This cost functionattempts to estimate the direct attenuation of the air lightwhich is applied to the scene for visibility enhancement In[10] the dark channel prior (DCP) is proposed for singleimage haze removal This prior based on some statisticalobservations of outdoor haze-free images is combined withthe haze imaging model to recover a haze-free image

Hindawi Publishing CorporationJournal of SensorsVolume 2016 Article ID 3894832 9 pageshttpdxdoiorg10115520163894832

2 Journal of Sensors

(a) (b)

Figure 1 (a) Scene visibility without fog (b) Scene visibility under foggy weather conditions

The authors in [11] propose another single image dehazingmethod which is based on [10] They improve the dehazingperformance by modifying the DCP method by introducinga single median filter operation They also study the effectsof dehazing on image and video coding In [12] restorationof hazy scenes from a single image is proposed by definingthe image through a scene transmissionmodel that takes intoaccount the surface shading A general survey of vision basedvehicle detection methods for intelligent driver assistancesystems is presented in [13] Finally in [14 15] the authorspropose enhanced visibility algorithms that take fog effectsinto account and are particularly well suited for road images

In this paper we present a novel approach to improve thevisibility of the scene in an image degraded by foggy weatherconditions We target enhanced visibility by generating anapproximate model of the fog composition in the scene witha deep neural network [16] This generalized model is thenused for the restoration of scene quality in the image Ourproposed method performs this recovery of image scene inreal time and it does not require any additional informationThe proposed method is robust as it achieves good result fora large set of unseen foggy images

2 Defogging by Deep Neural Network

21 Artificial Neural Networks Artificial neural networks[17ndash19] are complex mathematical systems which tend toelectronically simulate the working of a biological nervoussystem These networks are made up of a large number ofvery simple computational units called the artificial neuronsThe number of artificial neurons in a network can range froma few hundred to a several thousand artificial neurons Aneural network is formed by interconnecting these hundredsand thousands of artificial neurons in different topologiesOne such interconnected network is shown in Figure 2It is through this interconnection of simple computationalelements that the high computational complexity of theneural networks is realized Artificial neural networks arenowbeing researched to provide solution to various problemslike function approximation regression analysis time seriesprediction classification pattern recognition optimizationdecision making data processing filtering and clustering

and categorizing to name but a fewThe advent of immenselyhigh CPU processing abilities has made it possible to realizemore deep architectures [16 20] for the neural networksand hence more mathematically complex models can beaccomplishedAdeep neural network hasmany hidden layersof neurons between the input layer and the output layer

22 Deep Neural Network for Visibility Enhancement Wepresent a deep neural network that accepts a foggy image asan input models the corresponding fog composition in thescene and produces a defogged version of the scene in theimage as an output The architecture of the proposed deepneural network for image defogging is shown in Figure 2The network consists of an input layer an output layerand 119899 number of hidden layers sandwiched in between thetwo The multiple hidden layers of the deep neural networkare advantageous in realizing more efficient representationfor the corresponding fog function The learning problemconsists of finding the optimal combination of weights sothat the network function 120575 approximates a given function119891 as closely as possible The network learns this givenfunction 119891 through some implicit examples In this paperthe deep neural network is tailored to solve the visibilityenhancement problem by training it on several foggy imagesand their corresponding original images (input-output pairs)In order to learn the generalized fog function and produce thecorresponding defogged images the foggy images are dividedinto 119872 nonoverlapping blocks of size 119873 times 119873 pixels Eachblock is normalized to a range that optimizes the learningand results of the DNN The DNN is customized for a one-dimensional vector input therefore the two-dimensionalblock is transformed into a one-dimensional vector 119883 byrasterizationThis vector119883 is presented as an input pattern tothe networkTheweights of theDNNare initialized randomlyand the input pattern is forward propagated through themul-tiple hidden layers of the DNN to generate the propagationoutput activation The activation function employed in ourDNN is the hyperbolic tangent transfer function which isgiven by

T (119909) =119890119909

minus 119890minus119909

119890119909+ 119890minus119909 (1)

Journal of Sensors 3

Defogged imageFoggy image

Function approximation

Figure 2 Deep neural network architecture for fog removal

The tangent hyperbolic function produces the scaled outputover the minus1 to +1 closed range This function shares manyproperties of the sigmoid function but because the outputspace of this tangent function is broader it may be moreefficient for modeling complex nonlinear relations moreabstractly This input pattern 119883 produces an output thatis different from the target output (original Image) Theerror function of the network is the combination of errorscontributed by all the hidden nodes of the network and isgiven as

119864 =

12sum

119909

1003817100381710038171003817actual output minus desired output100381710038171003817

1003817

2 (2)

The combination of weights which minimizes the error func-tion is considered to be the solution of learning In this paperthis optimum set of weights is achieved by the backpropaga-tion algorithm The backpropagation is an iterative gradientdescent algorithm in which the output error signals aretransmitted backwards from the output layer to each node inthe hidden layer that immediately contributed to the outputlayer This backward propagation is continued layer by layeruntil each node in the network has received an error signalthat describes its relative contribution to the overall errorOnce the error signal for each node has been determinedthe errors are then used by the nodes to update the valuesfor the weights of the network This backpropagation oferrors and the updating of weights continue until the value oferror function becomes sufficiently small The training of thenetwork is stopped and the new input patterns are presentedto the network The trained network can approximate andremove the fog in these input patterns for better visibilityTheflow chart for the visibility enhancement DNN is shown inFigure 3

3 Experimental Results

Experiments were carried out on images obtained from theFRIDA database [14] The FRIDA database consists of sets

of synthetic images with and without fog The evaluation ofthe proposed method is carried out on gray scale imagesThe size of each image was 640 times 480 pixels The imagedata set was divided into training set images and the test setimages The learning of deep neural network for defoggingpurpose was carried out by the training set Architecturesbased onmultiple hidden layers (5 to 8)with various numbersof hidden nodes (16 to 128) were trained The convergence ofthe network was checked on the basis of mean square error(MSE) Once the network learnt the generalized fog functionthe results were applied to the test images For evaluationpurpose we show the results in Figures 4ndash10 In these figuresthe areas of interest are shown enclosed in rectangles Theproposed solution can be evaluated by analyzing these areasFigure 4(a) shows the original scene in the image withoutfog while Figure 4(b) shows the scene affected by fog It canbe seen clearly that the background in Figure 4(b) is fadedand is mostly invisible The tree enclosed in the rectanglein Figure 4(a) is barely visible in Figure 4(b) The majorportion of the building (front) which is visible in Figure 4(a)is completely invisible in Figure 4(b) Also other sections ofthe building enclosed in rectangles in Figure 4(a) are invisiblein Figure 4(b) Figure 4(c) represents the scene recovered byour deep neural network The building that was invisible inthe foggy version is recovered in the defogged version Thetree is visible and other areas enclosed in rectangles in thefigure are also partially recovered by the network Figures5(a) and 5(b) show the original scene and the fogged scenerespectively The front building which is clearly visible in theoriginal scene that is Figure 5(a) is faded in the fogged scenewhile the building behind it is not visible in the scene ofFigure 5(b)The smaller palm tree is also invisible in the foggyversionThe house and trees at the left-hand side of the foggyimage are almost invisible The traffic signs and some minorstructures are missing in the foggy scene In Figure 5(c)the fog is removed by the deep neural network The fadedfront building has improved visibility in the recovered imageThe building behind the front building which was not visible


Save

the n

etw

ork

wei

ghts

max iterations

Start

Normalization

Rasterization (1D input pattern)

Initialize training iteration counter

Initialize the weights of the DNN randomly

Present input pattern and compute the error

Backpropagation to the output layer

Backpropagation to the hidden layers

Update weights

Stop

Normalization


Present inputs pattern

Retrieve the optimized weights values

Run the DNN with these optimized weights

Image reproduced with scene recovery

End

Stop network training

Save

the n

etw

ork

wei

ghts

Training sessionTrained network session

acceptable errorError le

Divide the training image into Ntimes N blocks

Iterations ge

Divide the foggy image into Ntimes N blocks

Itera

tion=

itera

tion+1

Figure 3 Flow chart for proposed visibility enhancement DNN

previously can be clearly seen in our recovered scene Thesmaller palm tree is visible The house and tree at the left-hand side of the image are also recovered successfully Thetraffic signs and the minor structures that were invisible dueto fog are also recovered to some extent in the defoggedscene Figure 6(a) shows the original scene while Figure 6(b)represents the image with degraded visibility due to fogThe trees enclosed in the rectangles on the left-hand side ofFigure 6(a) are barely visible in Figure 6(b)The cars enclosedin the rectangles in the original image are invisible in thedegraded image Also the structuresbuildings enclosed inrectangles in Figure 6(a) are invisible in the degraded versionThe same scene with enhanced visibility is presented inFigure 6(c) As can be seen the trees which are barely visiblein the foggy image have been recoveredThe cars which werepreviously invisible can be spotted in the recovered imageAlso the structuresbuildings missing in the foggy image areretrieved by our DNN successfully Figure 7(a) shows theoriginal scene in the image without any fog and Figure 7(b)

represents the scene with reduced visibility due to foggyconditions Starting from the left-hand side of the image thetree which is visible in the original image is barely visible inthe degraded image The house and the tree adjacent to it areinvisible in the degraded imageThe palm tree is also missingin the degraded imageThe pole tree and the rear view of thebuilding on the right-hand side are severely degraded and areinvisible in the foggy image Figure 7(c) represents the scenerecovered in the image by the DNN Starting from the left-hand side the treewhich is barely visible has been recovered inthe enhanced version The house and adjacent tree have alsobeen retrieved in the reproduced image although the detailsaremissing but the structures are visibleThe palm tree whichwas previously invisible is present in the recovered sceneThepole tree and rear portion of the building are also recoveredand are clearly visible in the reproduced image Figures 8(a)8(b) and 8(c) show the original image the image affectedby foggy conditions and the image recovered by our DNNCompared to Figure 8(a) in Figure 8(b) the background


(a) (b)

(c)

Figure 4 (a) Original scene without fog (b) Scene visibility degraded under fog (c) Visibility enhancement by deep neural network

(a) (b)

(c)



(a) (b)

(c)


(a) (b)

(c)



(a) (b)

(c)


that is buildings houses trees vegetation and cars enclosedin the rectangular boxes is barely visible or invisible whilein Figure 8(c) our proposed DNN has recovered the aboveregions successfully Figures 9(a) and 9(b) represent theoriginal image and its foggy version respectively The housesat the end of the lane in the left-hand side of Figure 9(a)are almost invisible in Figure 9(b) The poles and trafficsignals are also invisible The building at the right-hand sideis faded and the trees behind it are not visible due to fog Thecars in the scene are barely visible In our recovered imagethat is Figure 9(c) the visibility of the scene is enhancedThe houses at the end of the lane are now quite visibleThe poles and traffic signals are recovered from the foggyversion The visibility of the building at the right-hand sideis improved and the trees behind it are now visible The carsin the recovered image are more detectable than the foggyversion Figure 10(a) represents the original scene withoutfog Figure 10(b) represents the image scene degraded by fogThe building at the right-hand side is faded due to fog andis barely visible The tree behind the building at the right-hand side is invisible The traffic sign is invisible in the foggyimage and a large section of buildings and the palm treeis also missing in the left-hand side of the foggy image InFigure 10(c) that is the scene reproduced by the DNN thebarely visible building on the right-hand side of the image islargely visible The tree behind is also recovered The trafficsign is also visible in the reproduced image The rear sectionof buildings and the tree on left-hand side have also beenrecovered successfully by our DNN

4 Conclusions

In this paper we propose a novel approach for restoringthe visibility of foggy images with the help of deep neuralnetworks This idea is an initial attempt to model the fogfunction for scene recovery with deep neural networks Themethod recovers the scene structure simply with a singleimage input operates in real time and generalizes wellThe restored images showed good recovery of the scenefrom the foggy images In some of the images the proposedtechnique showed limitations in reproducing the exact detailsof some objects in the scene but nevertheless they werefairly recognizable This method can be tailored to be used inapplications designed for driving assistant systems intelligentvehicle systems outdoor surveillance systems and so forthIn future works we are going to use this idea to develop moreadvanced frameworks for weather removal vision systemsbased on deep neural networks

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This research was supported by the MSIP (Ministry ofScience ICT and Future Planning) Republic of Korea under


(a) (b)

(c)


(a) (b)

(c)



the project for Technical Development of Information Com-munication and Broadcasting supervised by IITP (Instituteof Information and Communications Technology Promo-tion) (IITP 2015-B0101-15-1377)

References

[1] S G Narasimhan and S K Nayar ldquoVision and the atmosphererdquoInternational Journal of Computer Vision vol 48 no 3 pp 233ndash254 2002

[2] S G Narasimhan and S K Nayar ldquoChromatic framework forvision in bad weatherrdquo in Proceedings of theIEEE Conferenceon Computer Vision and Pattern Recognition (CVPRrsquo 2000) pp598ndash605 June 2000

[3] S G Narasimhan and S K Nayar ldquoContrast restorationof weather degraded imagesrdquo IEEE Transactions on PatternAnalysis and Machine Intelligence vol 25 no 6 pp 713ndash7242003

[4] S K Nayar and S G Narasimhan ldquoVision in bad weatherrdquoin Proceedings of the 7th IEEE International Conference onComputer Vision (ICCV rsquo99) vol 2 pp 820ndash827 September1999

[5] Y Y Schechner S G Narasimhan and S K Nayar ldquoInstantdehazing of images using polarizationrdquo in Proceedings of theIEEE Computer Society Conference on Computer Vision andPattern Recognition vol 1 pp 325ndash332 December 2001

[6] S Shwartz E Namer and Y Y Schechner ldquoBlind haze separa-tionrdquo in Proceedings of the IEEE Computer Society Conference onComputer Vision and Pattern Recognition (CVPR rsquo06) vol 2 pp1984ndash1991 June 2006

[7] S G Narasimhan and S K Nayar ldquoInteractive deweatheringof an image using physical modelsrdquo in Proceedings of the IEEEWorkshop Color and Photometric Methods in Computer Visionin Conjunctionwith IEEE International Conference on ComputerVision October 2003

[8] J Kopf B Neubert B Chen et al ldquoDeep photo model-basedphotograph enhancement and viewingrdquo ACM Transactions onGraphics vol 27 no 5 article 116 2008

[9] R T Tan ldquoVisibility in bad weather from a single imagerdquo inProceedings of the 26th IEEEConference onComputer Vision andPattern Recognition (CVPR rsquo08) June 2008

[10] K He J Sun and X Tang ldquoSingle image haze removal usingdark channel priorrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo09) pp 1956ndash1963 IEEE Miami Fla USA June 2009

[11] K B Gibson D T Vo and T Q Nguyen ldquoAn investigation ofdehazing effects on image and video codingrdquo IEEE Transactionson Image Processing vol 21 no 2 pp 662ndash673 2012

[12] R Fattal ldquoSingle image dehazingrdquoACMTransactions on Graph-ics vol 27 no 3 pp 988ndash992 2008

[13] Z Sun G Bebis and R Miller ldquoOn-road vehicle detectiona reviewrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 28 no 5 pp 694ndash711 2006

[14] J-P Tarel N Hautiere A Cord D Gruyer and H HalmaouildquoImproved visibility of road scene images under heterogeneousfogrdquo in Proceedings of the IEEE Intelligent Vehicles Symposium(IV rsquo10) San Diego Calif USA June 2010

[15] N Hautiere J-P Tarel and D Aubert ldquoMitigation of visibilityloss for advanced camera-based driver assistancerdquo IEEE Trans-actions on Intelligent Transportation Systems vol 11 no 2 pp474ndash484 2010

[16] G Hinton L Deng D Yu et al ldquoDeep neural networksfor acoustic modeling in speech recognitionrdquo IEEE SignalProcessing Magazine vol 29 no 6 pp 82ndash97 2012

[17] S O HaykinNeural Networks and Learning Machines PrenticeHall 3rd edition 2008

[18] Y S Abu-Mostafa M M Ismail and H-T Lin Learning fromData AMLBook 2012

[19] P H Sydenham and R Thorn Handbook of Measuring SystemDesign vol 3 John Wiley amp Sons Chichester UK 2005

[20] Y Bengio ldquoLearning deep architectures for AIrdquo Foundationsand Trends in Machine Learning vol 2 no 1 pp 1ndash27 2009

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Navigation and Observation



DistributedSensor Networks



(a) (b)

Figure 1 (a) Scene visibility without fog (b) Scene visibility under foggy weather conditions

The authors in [11] propose another single image dehazingmethod which is based on [10] They improve the dehazingperformance by modifying the DCP method by introducinga single median filter operation They also study the effectsof dehazing on image and video coding In [12] restorationof hazy scenes from a single image is proposed by definingthe image through a scene transmissionmodel that takes intoaccount the surface shading A general survey of vision basedvehicle detection methods for intelligent driver assistancesystems is presented in [13] Finally in [14 15] the authorspropose enhanced visibility algorithms that take fog effectsinto account and are particularly well suited for road images

In this paper we present a novel approach to improve thevisibility of the scene in an image degraded by foggy weatherconditions We target enhanced visibility by generating anapproximate model of the fog composition in the scene witha deep neural network [16] This generalized model is thenused for the restoration of scene quality in the image Ourproposed method performs this recovery of image scene inreal time and it does not require any additional informationThe proposed method is robust as it achieves good result fora large set of unseen foggy images

2 Defogging by Deep Neural Network

21 Artificial Neural Networks Artificial neural networks[17ndash19] are complex mathematical systems which tend toelectronically simulate the working of a biological nervoussystem These networks are made up of a large number ofvery simple computational units called the artificial neuronsThe number of artificial neurons in a network can range froma few hundred to a several thousand artificial neurons Aneural network is formed by interconnecting these hundredsand thousands of artificial neurons in different topologiesOne such interconnected network is shown in Figure 2It is through this interconnection of simple computationalelements that the high computational complexity of theneural networks is realized Artificial neural networks arenowbeing researched to provide solution to various problemslike function approximation regression analysis time seriesprediction classification pattern recognition optimizationdecision making data processing filtering and clustering

and categorizing to name but a fewThe advent of immenselyhigh CPU processing abilities has made it possible to realizemore deep architectures [16 20] for the neural networksand hence more mathematically complex models can beaccomplishedAdeep neural network hasmany hidden layersof neurons between the input layer and the output layer

22 Deep Neural Network for Visibility Enhancement Wepresent a deep neural network that accepts a foggy image asan input models the corresponding fog composition in thescene and produces a defogged version of the scene in theimage as an output The architecture of the proposed deepneural network for image defogging is shown in Figure 2The network consists of an input layer an output layerand 119899 number of hidden layers sandwiched in between thetwo The multiple hidden layers of the deep neural networkare advantageous in realizing more efficient representationfor the corresponding fog function The learning problemconsists of finding the optimal combination of weights sothat the network function 120575 approximates a given function119891 as closely as possible The network learns this givenfunction 119891 through some implicit examples In this paperthe deep neural network is tailored to solve the visibilityenhancement problem by training it on several foggy imagesand their corresponding original images (input-output pairs)In order to learn the generalized fog function and produce thecorresponding defogged images the foggy images are dividedinto 119872 nonoverlapping blocks of size 119873 times 119873 pixels Eachblock is normalized to a range that optimizes the learningand results of the DNN The DNN is customized for a one-dimensional vector input therefore the two-dimensionalblock is transformed into a one-dimensional vector 119883 byrasterizationThis vector119883 is presented as an input pattern tothe networkTheweights of theDNNare initialized randomlyand the input pattern is forward propagated through themul-tiple hidden layers of the DNN to generate the propagationoutput activation The activation function employed in ourDNN is the hyperbolic tangent transfer function which isgiven by

T (119909) =119890119909

minus 119890minus119909

119890119909+ 119890minus119909 (1)






119864 =

12sum

119909


1003817

2 (2)






Save

the n

etw

ork

wei

ghts

max iterations

Start

Normalization







Update weights

Stop

Normalization






End


Save

the n

etw

ork

wei

ghts




Iterations ge


Itera

tion=

itera

tion+1





(a) (b)

(c)


(a) (b)

(c)



(a) (b)

(c)


(a) (b)

(c)



(a) (b)

(c)



4 Conclusions




Acknowledgment



(a) (b)

(c)


(a) (b)

(c)




References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation














119864 =

12sum

119909


1003817

2 (2)






Save

the n

etw

ork

wei

ghts

max iterations

Start

Normalization







Update weights

Stop

Normalization






End


Save

the n

etw

ork

wei

ghts




Iterations ge


Itera

tion=

itera

tion+1





(a) (b)

(c)


(a) (b)

(c)



(a) (b)

(c)


(a) (b)

(c)



(a) (b)

(c)



4 Conclusions




Acknowledgment



(a) (b)

(c)


(a) (b)

(c)




References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Save

the n

etw

ork

wei

ghts

max iterations

Start

Normalization







Update weights

Stop

Normalization






End


Save

the n

etw

ork

wei

ghts




Iterations ge


Itera

tion=

itera

tion+1





(a) (b)

(c)


(a) (b)

(c)



(a) (b)

(c)


(a) (b)

(c)



(a) (b)

(c)



4 Conclusions




Acknowledgment



(a) (b)

(c)


(a) (b)

(c)




References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










(a) (b)

(c)


(a) (b)

(c)



(a) (b)

(c)


(a) (b)

(c)



(a) (b)

(c)



4 Conclusions




Acknowledgment



(a) (b)

(c)


(a) (b)

(c)




References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










(a) (b)

(c)


(a) (b)

(c)



(a) (b)

(c)



4 Conclusions




Acknowledgment



(a) (b)

(c)


(a) (b)

(c)




References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










(a) (b)

(c)



4 Conclusions




Acknowledgment



(a) (b)

(c)


(a) (b)

(c)




References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










(a) (b)

(c)


(a) (b)

(c)




References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









research article visibility enhancement of scene...

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