Comparative study on image fusion methods in spatial domain


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Comparative study on image fusion methods in spatial domain

  1. 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME161COMPARATIVE STUDY ON IMAGE FUSION METHODS IN SPATIALDOMAINProf. Keyur N. Brahmbhatt1Assistant Professor, I.T DepartmentB.V.M Engineering CollegeVallabhVidyaNagar-388120, Gujarat, India.Dr. Ramji M. Makwana2Associate Professor, Computer DepartmentADIT Engineering CollegeVallabhVidyaNagar-388120, Gujarat, India.ABSTRACTImage fusion is a process of combining images, obtained by sensors of differentwavelengths simultaneously viewing of the same scene, to form a composite image. Thecomposite image is formed to improve image content and to make it easier for the user todetect, recognize, and identify targets and increase his situational awareness. The researchactivities are mainly in the area of developing fusion algorithms that improves theinformation content of the composite imagery, and for making the system robust to thevariations in the scene, such as dust or smoke, and environmental conditions, i.e. day or andnight. This paper is structured in the following way: section 1 gives introduction to imagefusion. Section 2 provides details on several fusion algorithms. Section 3 defines a set ofimage fusion measures of effectiveness. Section 4 provides a comparative study of the fusiontechniques in spatial domain finally; Section 5 provides a summary of the paper and its mainconclusions.Keywords: Spatial domain, Select Maximum/minimum, PCA, HIS, Bovey TransformINTERNATIONAL JOURNAL OF ADVANCED RESEARCH INENGINEERING AND TECHNOLOGY (IJARET)ISSN 0976 - 6480 (Print)ISSN 0976 - 6499 (Online)Volume 4, Issue 2 March – April 2013, pp. 161-166© IAEME: Impact Factor (2013): 5.8376 (Calculated by GISI)www.jifactor.comIJARET© I A E M E
  2. 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME162I. INTRODUCTIONImage fusion means the combining of two images into a single image that has themaximum information content without producing details that are non-existent in the givenimages. [2] With rapid advancements in technology, it is now possible to obtain informationfrom multi source images to produce a high quality fused image with spatial and spectralinformation. Image Fusion is a mechanism to improve the quality of information from a set ofimages. Important applications of the fusion of images include medical imaging, microscopicimaging, remote sensing, computer vision, and robotics.[7] Recently, Discrete WaveletTransform (DWT) and Principal Component Analysis (PCA), Morphological processing andCombination of DWT with PCA and Morphological techniques have been popular fusion ofimage. These methods are shown to perform much better than simple averaging, maximum,minimum. [1]II. IMAGE FUSION ALGORITHMA. Average MethodHere, the resultant image is obtained by averaging every corresponding pixel in theinput images [4]• Advantage1) It is very simple method.2) Easy to understand and implement.3) Averaging works well when images to be fused from same type of sensor and containadditive noise.4) This method proves good for certain particular cases where in the input images have anoverall high brightness and high contrast.• Disadvantages1) It leads to undesirable side effect such as reduced contrast.2) With this method some noise is easily introduced into the fused image, which will reducethe resultant image quality consequently. [3]B. Select Maximum/Minimum MethodA selection process if performed here wherein, for every corresponding pixel in theinput images, the pixel with maximum/minimum intensity is selected, respectively, and is putin as the resultant pixel of the fused image. [4]• Advantage1) Resulting in highly focused image output obtained from the input image as compared toaverage method [6]• Disadvantage1) Pixel level method are affected by blurring effect which directly affect on the contrast ofthe image [6]
  3. 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME163C. Brovey TransformBrovey transform (BT) , also known as color normalized fusion, is based on thechromaticity transform and the concept of intensity modulation .It is a simple method tomerge data from different sensors, which can preserve the relative spectral contributions ofeach pixel but replace its overall brightness with the high spatial resolution image .As appliedto three MS bands, each of the three spectral components (as RGB components) is multipliedby the ratio of a high-resolution co-registered image to the intensity component I of the MSdata [3]• Advantages1) It is a simple method to merge the data from different sensors.2) This method is simple and fast.3) It provide superior visual and high resolution multispectral image.4) Very useful for visual Interpretation.• Disadvantages1) This method ignores the requirement of high quality synthesis of spectral information.2) It produces spectral distortion. [3]D. Intensity Hue Saturation (IHS)It is most popular fusion methods used in remote sensing. The fusion is based on theRGB-IHS conversion model, whose various mathematical representations have beendeveloped .No matter which conversion model is chosen, the principle of the IHStransformation to merge images attributes to the fact that the IHS color space is catered tocognitive system of human beings and that the transformation owns the ability to separate thespectral information of an RGB composition in its two components H and S, while isolatingmost of the spatial information in the I component. In this method three MS bands R, G andB of low resolution Image are first transformed into the IHS color coordinates, and then thehistogram - matched high spatial resolution image substitutes the intensity image whichdescribes the total color brightness and exhibits as the dominant component a strongsimilarity to the image with higher spatial resolution. Finally, an inverse transformation fromIHS space back to the original RGB space yields the fused RGB image, with spatial details ofthe high resolution image incorporated into it .The intensity I defines the total colorbrightness and exhibits as the dominant component . After resolution using the highresolution data, the merge result is converted back into the RGB After applying HIS. [3]• Advantages1) It provides high spatial quality.2) It is a simple method to merge the images attributes.3) It provides a better visual effect.4) It gives the best result for fusion oh remote sensing images.• Disadvantages1) It produces a significant color distortion with respect to the original image.2) It suffers from artifacts and noise which tends to higher contrast.3) The major limitation that only three bands are involved [3]
  4. 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME164E. Principal Component Analysis AlgorithmPrincipal component analysis (PCA) is a vector space transform often used to reducemultidimensional data sets to lower dimensions for analysis. It reveals the internal structureof data in an unbiased way.• Advantages1) This method is very simple to use and the images fused by this method have high spatialquality.2) It prevents certain features from dominating the image because of their large digitalnumbers.• Disadvantages1) It suffers from spectral degradation.2) This method is highly criticized because of the distortion of the spectral Characteristicbetween the fused images and the original low resolution Images. [3]III. MEASURING TECHNIQUEA. ENTROPY (EN)Entropy is an index to evaluate the information quantity contained in an image. If thevalue of entropy becomes higher after fusing, it indicates that the information increases andthe fusion performances are improved. Entropy is defined as:-L-1E = - ∑ pi log 2 pii=0Where L is the total of grey levels, p= {p0, p1…pL-1} is the probability distribution of eachlevel. [1]B.MEAN SQUARED ERROR (MSE)The mathematical equation of MSE is giver by the equationm nMSE = 1 ∑ ∑ (Aij-Bij)2mn i=1 j=1Where, A - the perfect image, B - the fused image to be assessed, i – pixel row index, j – pixelcolumn index, m, n- No. of row and column [1][5]C. NORMALIZED CROSS CORRELATION (NCC)Normalized cross correlation are used to find out similarities between fused image andregistered image is given by the following equation [1]
  5. 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME165m n∑ ∑ (Aij * Bij)NCC = i=1 j=1m n∑ ∑ (Aij)2i=1 j=1IV. COMPARATIVE STUDY OF SPATIAL IMAGE FUSION TECHNIQUEHere we have made comparison of various image fusion methods in spatial domain.MeasuringParameterAverageMethodMaxima/MinimamethodBrovey Method IHS PCASimplicity Simple and easyto implementSimple method simple and fastmethodSimplemethodSimplemethodType ofresourcesFused imagefrom same typeof sensorFused imagefrom sametype of sensorMerge the datafromDifferentsensors.Merge thedata fromDifferentsensors.Disadvantage Reducedcontrast.Create blurringeffectsspectraldistortioncolordistortionspectraldegradationDisadvantage If some noise isintroduced , itwill reduce theresultant imagequalityconsequentlyIt has higherpixel intensitybut it does notmeans alwaysgive betterinformation.This methodignores therequirement ofhigh qualitysynthesis ofspectralinformation.It suffersfromartifacts andnoise whichtends tohighercontrast.Resultingimagedoes notpreservefaithfully thecolors foundin the originalimagesV. CONCLUSIONAlthough selection of fusion algorithm is problem dependent but this review resultsthat spatial domain provide high spatial resolution and easy to perform, but spatial domainhave image blurring problem and their outputs are less informative.VI. REFERENCES[1] Deepak Kumar Sahu, M.P.Parsai, “Different Image Fusion Techniques –A CriticalReview”, IJMER, Vol. 2, Issue. 5, Sep.-Oct. 2012 pp-4298-4301 ISSN: 2249-6645[2] Firooz Sadjadi, “Comparative Image Fusion Analysis”[3] Nupur Singh , Pinky Tanwar, “Image Fusion Using Improved Contourlet TransformTechnique” IJRTE ISSN: 2277-3878, Volume-1, Issue-2, June 2012
  6. 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME166[4]Shivsubhamani,K.P.Soman “Implementation and Comparative Study of Image FusionAlgorithms”, International Journal of Computer Applications (0975 – 8887) Volume 9–No.2, November 2010[5] Shivsubramani Krishnamoorthy, Development of Image Fusion Techniques AndMeasurement Methods to Assess the Quality of the Fusion[6] Vidhya K P , Saritha E S, “A Comparative Study on Medical Image Fusion Technique.[7] Xydeas, C., and Petrovic, V., “Objective Pixel-level Image Fusion PerformanceMeasure,” Sensor Fusion: Architectures, Algorithms, and Applications IV, SPIE Vol. 4051,pp. 89-98, 2000.[8] Benayad Nsiri, Salma Nagid and Najlae Idrissi, “New Approach to Multispectral ImageFusion Based on a Weighted Merge” International Journal of Electronics and CommunicationEngineering & Technology (IJECET), Volume 4, Issue 1, 2013, pp. 25 - 34, ISSN Print:0976- 6464, ISSN Online: 0976 –6472.[9] Dr. Sudeep D. Thepade and Jyoti S.Kulkarni, “Novel Image Fusion Techniques usingGlobal and Local Kekre Wavelet Transforms”, International journal of ComputerEngineering & Technology (IJCET), Volume 4, Issue 1, 2013, pp. 89 - 96, ISSN Print: 0976– 6367, ISSN Online: 0976 – 6375.[10] I.Suneetha and Dr.T.Venkateswarlu, “Spatial Domain Image Enhancement usingParameterized Hybrid Model”, International Journal of Electronics and CommunicationEngineering & Technology (IJECET), Volume 3, Issue 2, 2012, pp. 209 - 216, ISSN Print:0976- 6464, ISSN Online: 0976 –6472.