Effective Application of Retinex Transformer with One-Stage In Low- Light Image Enhancement by IRJET Journal

Effective Application of Retinex Transformer with One-Stage In LowLight Image Enhancement

Dr. R. Senthilkumar,

Associate Professor& Head, Department of Computer Science, Sree Amman Arts & Science College, Erode, Tamil Nadu, India.

Abstract - At present days most the researchers arecarried out a lot of color enhancement of gray images in diverse field. We have most sophisticated methodologies in images enhancement but it may lack in expected result accuracy .The basic ideas is fetching out the hidden details present in the input image and also to highlight the interesting factors that may be implemented in some need calculation like weather, earthquake etc.. This method mainly corrects and secures the gray scale color transformation method. Other important enhancementtechniquelike masking,histogrammanipulation also plays a tedious role removing the noise present in the image then as further it is implemented for fetching information presentinthe image. This alsoprevents pixel selftransformationmethodrainbowcoding,metalcodingandalso pseudo color enhancement algorithm based on frequency domain. In current centuries signal as well as image processing is based on fractional calculus has attracted extensive attention. Color Enhancementisrealizedbyutilizing the constructed high gray scale enhancement algorithm when it is been analyzed with the traditional enhancement method the image results with a prominent difference in the performance which is relatively the objective indicatorswhen dealing with image enhancement with the impression of low light t. The proposed method effectively with traditional jet codingas well as HSV pseudo color methods as well as find out the brightness of image, brightness of the distortion.

Key Words: Low Light Enhancement, Histogram Equalization,Deeplearning,OneStageRetinex,Traditional methods,ImageEnhancement.

1. INTRODUCTION

The classic Retinex method only shows the way of pixel representation andthepredictededgesofanimageotbe enhanced .These factors make it harder for computers to recognize the images and for humans to do so as well. Enhanced approaches for low light photos were typically used in deep learning improvement methods, along with regularly used evaluation indicators for nighttime image enhancement.Issuesandtheadvancementofdeeplearning techniques for improving 3D picture angles and low light photographs. (e.g. [1]). Spatial Domain performs manipulation of pixels directly in the image in the image planeitself.Torepresentamoreclearspecificationofimage theperformlinearandnon-linearoperations.The approach

in enhancementisfrequencyDomain herethe enhanceof an image is taken as f(x, y) and then the image again is implemented to manipulate as linear, position Then 2D convolutionisbeenperformed.Buttillweareinneedof more unique technique for images like neural network architecture and the learning algorithm which is been implementedindeeplearningaretakenintoconsideration. When implementing various deep learning image enchantment techniques. The images get have some very noisyandtofilterthoseimagessothatthenoisepresentin the image can be removed and the image appears much better.Insomeotherkindofapplicationstoenhancecertain characteristics of the image .Even versatile image enhancementtechniqueswherepresentinthissituations.In the proposed paper an additional enhancing technique is implementing with Retinex One Stage Retinex which has assigned a unique level of restoration and it follows an illuminate Retinex Transforms which work for setting the expectedhighestclarity oftheinputtaken

Spatial Domain:Itmainlyworkwithpixelstomanipulate directlyintheimageaccordingtoimageplaneitself.Tomake it more clear a unique specification of image is used the performlinearandnon-linearoperations

Frequency Domain: The enhance of an image is takenasf(x,y)andthenthe image againisimplementedto manipulate as linear, position Then 2D convolution is performed. As the advancement in enhancement the deep learningwhichrecognizesthecomplexdigitalimageswith respectivetothehumanbrainthat’shelpstopredictthedata patternofthe imagethathelpstoexplorefurtherinimage enhancement.TheOnestageRetinexhelpstoworksindepth indataretrieval fromanoiseimage.

2. HISTOGRAM EQUALIZATION AND RETINEX

2.1Bi-Histogram Equalization

It is the simplest and effectively proved as the basic enhancement technique in traditional methods of image enhancement.Wehaveimplementedheretodoenhancethe

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Fig 1: Comparison of Image Enhancement with different Datasets

basic or initial steps and effective steps in image enhancement method. We had taken Yeong-Teag Kim’s proposed method which states the Brightness locking BiHistogram

Equalization method [2] which has been based on the particular Mean value. It include partitioning an image X intotwosubimages,XandX.Thiscanberepresentedasthe imagesdecomposedarebeenequalizedand independently

composedlater.Thismethodaimstopreservethecontrast enhancement in the taken input image When considering the image brightness it may also sometimes subjected to obtainthemeanvaluebycomparingwiththecalculatedand prepredictedvalueswhichhasbeenderivedtocomparethe obtainedvalues.

2.2 DualisticSub-ImageHistogram Equalization

ThisparticularTechniqueinhistogramwasbeenadoptedby Yu Wang et al. implemented for image enhancement. It

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depends on the calculation of the Median value as representedin[3].

workswiththedecompositionofimageconsideredtobeX by can be represented by the below mentioned equations whichthetwosub-images,XLandXUarebeenobtainedthis shows the difference and then the differences are then calculatedwithameanvaluelyingbetween L and U .Then for further clarification further histogram plotting’s are carried out to the resultant means value present in the image. The above equation when implemented with the arrivedvalueswillgivestoinformationofthedecomposition level.

3. IMPLEMENTATION OF EDGE FILTER AND DECOMPOSITION

Low light image enhancement is considered to a tedious problem.Byimplementingedgefilteringanddecomposition methodsitembossand highlightstheboundaryareas and corresponding information hiding in the image The enhancedimageisobtainedmoreaccuratewhencompared withtheoriginalimage.Thishappensaccordingneedseven without considering the image degradation process and removingunimportantfeaturessothatthesuitsforspecific applications. The images taken at night time appear to be mostlywhiteandblack.Evensometimesisthedeviceused to shoot is not enough, it results in color blackness, and appears to be being stretched. By this impact there is no absoluteimprovementofcolorsaturation.Tosolveitwecan use a changing color space not taking color factors as an account.

InthisOistheobservedimageandS(⋅)is edge-preserving operation, D is the detail layer of the image, τ is an importantparameterit controlstheexpansionofthedetail layer,and E isthe imageaftertheaboveenhancement.The imagesignalprocessingmethodisprocessedbyaseriesof subtasksthatincludesDenoisingandwhitebalanceBased on deeplearning,astraightforwardconversionismadewiththe originalimageandconvertedintoacolor.Itcanbedefined as:

Inthis F is consideredtobetheproposednetworkand θ is datasetparameter.Wehavetotraindataassamplesinorder to check the effects in different aspects. Fromthespecific

examplesitisfoundtobeharderretrievingneededdata .To workwithsuchsituationanimageconformstohumanvisual is considered to be the objective image quality evaluation methodwhichisimplementedascommonlyusedevaluation method. Our deep learning method with one stage comes withtwophenomenonsignaltothenoiseratioandstructural equalitywhichhelpstochecktheimagequalitybycalculating the error between corresponding pixels. Thecalculationis expressedasfollows

4. RETINEX BASED TRANSFORMS

The basic concept capturing enhancing can be done byusingtraditionalmethodswhichiscurrentlyavailable.The inputimagetakenwillresultinaconfusedstageifthereis misunderstand between the image projected digitallyand absorbed in a wrong way this may also result in failureof predicting accurate information from the input image. To resolve this method named Retinex can be implemented whichavoidsthesecircumstances.Herethesemethodshas implementedwithtwopowerfulcarotenoidsprojectedhighly with concentrations in macula which is the seeing part of human eye. The important pre assigned task of Retinex is matching the illuminate value present in the image with respect to human eye and the screen. The luminance producesthereflectanceoftheimage.Wecanformulatethis as I as Retinex image S is the illumination and r is the reflectance coordinates. The light of the image can be adjustedtilltheuniformityisreachedbyadjustingthebelow factors.

5. DEEP LEARNING MODELS

Traditionalmethodstheimageperceptionarebeenproposed to explain the perceived color constancy of objects under varying illumination conditions. The One stage Retinex Transforms works with decoupling illumination and reflectioncomponentsoflow-lightimagesinvolvecomplex priorsbutthepresentedtheory,animagecanbedecomposed into to represent the object of the image with lighting conditions the illumination maps and reflectance part is converted to intrinsic property The quantified feature of RetinexTransformshelpsinlearningprocessbeenachieved byassigningweightsorparametersofa model aftermany iterations similarly to how human vision is believed to operate to analyze the object and perform a particular computationaltask.Thehumanretinasystemviewthecolor of the image enhanced by this algorithm is more in line compared with human visual characteristics used the brightness masking characteristic detail with presents the

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Retinextheory.Several approachesexisttoimplementthe Retinexprinciples,amongthesethemultistateRetinexwith colorrestorationalgorithm(MSRCR.)butwhencomparing with one stage the transforms apply the training data in different stage of enhancement process .The Image enhancementprocesscanbedivided onthebasicsofthe previousmentionedprocessing appliedonthe spatialfield and transformation field Transform techniques when appliedare beenfurther classified aswaveletdomainhasto becurveletdomainalgorithmsdependsonthedomainthe adjusted multi scaling parameters and also utilizing the visualstatisticalcharacteristicstomakeaclearrepresenting inner and the outer curves of the image. The radiant characteristics of OSR model to enhance the image to transformsbesidestheadjustmentoftheimage.

Thebrightnessofimageenhancement happensina secure way ithelpstotalprocessaswellasthedualtreecomposite. Thewavetransform fieldbyutilizingthenonlinearcontrast mappingismorecoefficientvalue.TheaboveImageshows theoutputthedifferentdatasetsareimpliedwiththeinput takenhasgaveadramaticrepresentationofnoisefreeimage representation. The Challenges associated with capturing both the visible and extended hyper spectral ranges are highlighted using the OSR emphasizing the complexities involved in image enhancement. When applied in various photographicinputimagestheRetinexmethodconstructsa thebettersolutionswhenexposedtoenhancement.

TheOneItuseaninvisibleorasilentparametertoworkin enhancementwhichtrytoproduceafeasibleoutput.Soitis considered to one of the straight forward method. stage Retinextechniquestakeadvantageofcalculativeoperations toachievedifferenteffectiveoutputasexpectedresults.Our contributionssummarizedasfollows:

1. Implemented Transformer based algorithm.

2. Packed with Retinex former from image restoration.

3. An expected compressive output was produced withOSR.

6. CONTRAST ADJUSTMENT WITH ONE STAGE

Inthispaperwealsotrytosolvetheissueshappenedby contrast which is also considered to the key factor which affectstheoriginalimageclarity.Tomakethisissueclear we areinasituationtoagainworkwithhistogramequation It isalsofoundthatthiscontrastadjustmentisalsobasedon thebalancecolorofretinalimage.Thecolorspacespresentin theimageisbeencategorizedwithRGBrelatedtolightness findingthecoloropponenttomatchtherangeof thelight presentintheimage.Inthispaperweapply CLAHEandthe histogram stretching algorithms .When applied with histogramthismethodstretchestheintensityvalueandtryto enhanceallthecolorandthebrightnesslevelpresent inthe image fromwhichwecanabletofindoutthecolorshades anditspercentageofpresenceintheimageforcalculating

Table 1: Comparison of Image Enhancement Methods

Fig 2: Image comparison after image Enhancement

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ContrastAdjustment=MaPI–MiPI

MaPI-MaximumPixelIntensity

MiPI-MimimumPixelIntensity

Theexpectedoutputshoesavastimprovementinaspectof contrastpresentintheoriginalimage.Thehistogramsare alsocalculatedforsmallregionalareasofpixels.Afterthis IntensityCorrectionIntensitySometimesnon-uniformityin satelliteimagesmaycauseduringtheacquisitionoftheinput image. This happens due to the noncooperation of the devices used and also artifacts caused by slow intensity variations. To solve this Maximization of Expectation algorithmisutilized tocorrectthevariationsofintensity. Thismethodwillnotmakeassumptionofthesequencestype and texture intensity so they can be used in all image process.ThismethodworkasexpectationStepmaximization step.ItisrelatedtotheK-meansmethodwhereasetofdata isrecalculatedtill the expected range arrives. Thesesteps arerepeatedtillreachingthetargetedoutputassatisfying theexpectedlevel.Asthe nextcontinuesstageofcontrast and correcting intensity, an enhanced method of noise removalisappliedItreducingthenoisewithoutaffectingor alteringthe imagecontentandalsothe edges,linesorother details are also not altered Anisotropic filter are used as filtering technique in digital images. The anisotropic diffusion algorithm introduces effects like blocking and evacuates structural and unwanted neighborhood information.Wecombinedwithedge-sensitivemethodsto make an effective noise removal process. Here the image featuresarebeensegmentedbysmootheningtheimageand enhancestheedgesoftheimage.

7. RELATED WORK

7.1

Low-light Image Enhancement

7.1.1. Plain Methods.

Usually called a mapping-based approach. Such a method enhances an image by modifying the distribution and dynamic range of the gray values of the pixels The main subclassesofthistypemethodincludelinearand nonlinear transformations Nonlinear Transformation with one stage Retinex.

7.1.2. Traditional Methods.

Deep learning when implemented in traditional image processingcanoftenprovideanoversolvedsolutionwhich some time does not match expected output. This feature processedbydeepnettechniqueofRetinexandwhenapplied to specific dataset will match the expected output which goesforfurtherrefinement.

7.1.3. Deep Learning Methods.

A deep Learning implemented Retinex decomposition methodhasbeeneffectivelylearnedtodecomposetothefine extendoftheimageintoreflectanceandilluminationinadata driven but it sometime produce an result more than the targeted output or more than the clarity that we are expectingwillrelayusinanconfusedstageofdefiningthe imagequality.TheexistingRetinexhasbeencomparedwith Retinex-Netwiththefourstateoftenmethods,includingthe de-hazing based method naturalness preserved enhancement algorithm, simultaneous reflectance and illumination estimation algorithm and illumination map estimation based algorithm provides a noise free visual comparison on three natural images. The reference image and finally compute the loss function and optimize the parameters,whicharchivecontinuouslyimprovingthecolor restorationperformanceafterbeenprocessed.Deeplearning

Fig 3: Contrast Adjustment comparison in image for One Stage Retinex

Fig 4: Implementation of One Stage Retinex Method

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techniquesforimageenhancementhave emergedasfinest output that gives us sophisticated results of artificial intelligence.

8. ONE STAGE RETINEX-BASED FRAMEWORK

The main goal of Image enhancement is a crucial and tediousprocesswhichworksoneffectivevisualizationand analysis.Animagecanbedecomposedintoareflectancein accordance with the Retinex theory where light I is decomposed to illumines L and reflection R and can be representedasbelow

RESEARCH PROCESS

Dataset

Thestudychadcarriedbytakingsamplephotographyimage 500X500 pixels to calculate and to estimate the one stage Retinexwithtraditionalanddeeplearningmethods. When using this one stage Retinex even the images captured at differenttimesofthedayandnightprovidedwith avariedof imagesproduce goodandadaptableilluminationconditions. Theoriginalimagehasastructureof fullHD1920×1080 resolution images which is been captured by using Sony Alpha6400camerasandshort with at50framespersecond. These images with processed with Faster R-CNN .COCO dataset is implemented to test the trained data. As a Final result, the dataset has been identified with the subjects of interestbyhandandselectedrepresentativeimagesforeach individual

InRetinextheory,aninputimagecanbeexplainedasvariety ofreflectioncomponentandilluminationcomponent,which canbeexpressedas theWorkflowoftheOnestageRetinex canbeimplementedasthebelowrepresentationwherewe can also perform various Deep Retinex scaling . Accurate estimationandaclearnormalizationarethetwoimportant factors behind the deep learning. The reflectance and illumination are taken from the input image and can be representedbyRepresentRcanberepresentedasreflectance andLcanbetakentorepresentillumination.Theedgehasan importantaspectwhichmaycauseblurringwhichalsoaffects theclarityofimagetheonestageRetinexalsoestablishthe edgecoordinateswhereIrepresenttheoriginalimage,R.

The Retinex former is implemented with on one stage Retinex-based Framework (ORF).It is composed of an illuminationestimator,acorruptionremover.AnIllumination Transformerisimplementedtoremovethenoisepresentin the input image .the frame work has a attention block in further classified into normalization and Multi processed modulethatdotheworkofnoiseremovalandenhancement of image The image enhancement can be successfully implemented in various fields that including important factorslikehealthcare,defense,andretailamongothers.The image data considering with its contrast and color are resulted as weak appearance when processed with traditional methods .The aim of imaging should produce a good visual representation when the input image visually screened by the human eye directly the image should produce a good visual impression subjected to the linear representationand rendering for a very restrictedclassof scenesthosewithoutany illuminationand restricted

Experimental Details

The process transformation includes the input image for nonlinear spatial processing. To calculate the One Stage Retinexanditsperformanceweuse aframeworkonNVIDIA GTX1060TiandIntelCorei7-7700HQ3.7GHzCPU.Thedata preprocessing has been executed using training data implementedusingtasklikescaling,rotationandcropping thenitisbeenprocessedintothepixelformatbywhichwe can observe an noise free image at the beginning of the process itself. The Process carried in such a way that the inputimagetakenisassignedastargetedinputandcompared withtheframeworkofOneStageRetinexbeenthesourceof thepapercalculatethenoiseandcolordistortionpresentin the input taken and produce an gowning histogram graph representation.Atestdatasettocomplete training and the performance of the model was executed to check the expectedlevelofperformance.Intheimagedecomposition networkmodule’strainingphase,alearningrateofbetween 10 and 4 are was chosen with a batch size of 8 was incorporated.The lightenhancementnetworkstructure,has used 10−3 learning rate and a batch size of 15 A learning rate of 10−4 was used and the batch size will be duringthetrainingofthecolorrestorationmodulehasbeen used. The human eyes may lose some details when the observation of anpictureisnot doneproperlyitmayalso behappenedbylowlight,wecalculated (perception-based image quality evaluator (PIQE), image quality measure metrics (mean-squared error (MSE), structural similarity (SSIM),andpeaksignal-to-noiseratio(PSNR))andlightness order error (LOE) to measure the quality of the enhanced imagesderived

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Table 2: Results of Image Quality Tested With Different Metrics

Table 2 shows the results of the different quality measure methodsandthevaluesrepresenttheaveragevalue.Thebest scoresarehighlightedinboldandthenearbestscoresare highlightedwithunderlinevalues

CONCLUSION

Nowconsideran example ifa greenapple forinstantly looks green efficienttechnique on collection of images by applyingacommoncolorfactor.Thisapproachaddressing valuesinitiallyrecoverssparsepixelcorrespondencesfrom inputimagesandarrangesthemintoamatrixfindingmissing entries. The parameters used here are viewed as white balancing parameters for each input image. The Retinex theory consider propose an well-developed model with combiningtodeepimageenhancementtechniquewillalways produce a good output The methods like decomposing, enhancing, and reconstructing .A network model with an encoding and decoding vector has been incorporated in convolutions connections to gather the information and it helps to reduce the details that has been lost during decomposition. On observing all the other algorithm out currentmethodalsoachievesthesuperiorresultsintermsof PSNR,SSIM,andNIQEimagequalityassessment metrics in image .Future research directions include expanding the dataset,optimizingnetworklossfunctions,enhancingmodel speed and efficiency and increasing the algorithm’s applicability across different domains Even in terms of PSNR,SSIM,andNIQEandothernoisedatatheinputtaken image suffers a vast impression of noise representation whichhastobeconsideredproduced.Andualdiscriminator whenappliedtolowlightimageenhancetheloss functionby incorporating lightning up technique to measure image distortionevenedgefilteringtechniquecanhelpstoachieve thesamenoisefreeimagequality.Thelow-lightimagetaken willbedividedeffectiveilluminationmappedcorresponding toreflectance.Theilluminationmodulewilldevelopcurvesto map finally they are been employed for iterative in illuminationmappingThenoisypartoftheimagetakenare beencorrectedandbyusing3DLUT-basedOneSageRetinex colorrestorationmethodologyhascanupwithansolutionof improvementintheinputimageasitsnoisewasclearedinan satisfactorywayandeveninlowlighttheonestageRetinex

estimatehiddendetailsoftheimage.Inthepapertheinput imageshasimprovedinnoise byeffectivelyenhancingthe image and the color distortion issues helps to develop subjectivevisibilitytocompared tootheralgorithmstoprove theefficiencyofthechosenalgorithmtheeffectiveprocessing of the information available in the image are been well improvedusingproposedalgorithmwhichhasimplemented definelyimproveandenhancethebrightnessofthelowlight image.

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