A COMPARATIVE STUDY OF MULTI-FOCUS, MULTI-RESOLUTION IMAGE FUSION TRANSFORMS AND METHODS
Multi-resolution image decomposition transforms are a popular approach to current image processing problems such as image fusion, noise reduction, and deblurring. Over the past few decades, new algorithms have been developed based on the wavelet transform to remedy its directional and shift invariant shortcomings (undecimated discrete wavelet transform is shift invariant). This study provides a comprehensive analysis of multi-focus image fusion techniques using six different multi-resolution decomposition transforms to determine the optimal transform for an image fusion application. The transforms investigated are the wavelet, double-density wavelet, dual-tree wavelet, curvelet, contourlet, and bandelet. Furthermore, for each transform, seven transform coefficient fusion algorithms are analyzed and the performance is evaluated using eight no-reference objective image fusion metrics. The transforms and algorithms selected are applied to a data set that has 27 pairs of multi-focus source images used for image fusion. By bringing together the transforms, fusion algorithms, and metrics presented in this study as derived separately from different authors, the study seeks to compare these methods. However, a complete comparison amongst the different transforms, algorithms, and metrics has not been found in any of the existing literature. Our goal is to provide useful insight into their applications in image fusion. The summary of the aggregated results indicates that (1) the curvelet is the most robust transform, (2) down-up and linear are the most effect methods of fusion, and (3) Tsallis is the best metric for multi-focus image fusion.
Peng, Kewu, and John C. Kieffer. "Embedded image compression based on wavelet pixel classification and sorting." IEEE Transactions on Image Processing 13, no. 8 (2004): 1011-1017.
Chrysafis, Christos, and Antonio Ortega. "Line-based, reduced memory, wavelet image compression." IEEE Transactions on Image processing 9, no. 3 (2000): 378-389.
Efstratiadis, Serafim N., Dimitrios Tzovaras, and Michael G. Strintzis. "Hierarchical partition priority wavelet image compression." IEEE transactions on image processing 5, no. 7 (1996): 1111-1123.
Lin, En-Ui, Michael McLaughlin, and Abdullah Ali Alshehri. "Medical image segmentation using multi-scale and super-resolution method." In 2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1-5. IEEE, 2014.
Daubechies, Ingrid. Ten lectures on wavelets. Vol. 61. Philadelphia: Society for industrial and applied mathematics, 1992.
Selesnick, Ivan W., Richard G. Baraniuk, and Nick C. Kingsbury. "The dual-tree complex wavelet transform." IEEE signal processing magazine 22, no. 6 (2005): 123-151.
Gnanadurai, D., and V. Sadasivam. "Image de-noising using double density wavelet transform based adaptive thresholding technique." International Journal of Wavelets, Multiresolution and Information Processing 3, no. 01 (2005): 141-152.
Saevarsson, Birgir Bjorn, Johannes R. Sveinsson, and Jon Atli Benediktsson. "Time invariant curvelet denoising." In Proceeding of the Nordic Signal Processing Symposium,(NORSIG 2004), vol. 117120. 2004.
Do, Minh N., and Martin Vetterli. "The contourlet transform: an efficient directional multiresolution image representation." IEEE Transactions on image processing 14, no. 12 (2005): 2091-2106.
Mallat, StÃ©phane, and Gabriel PeyrÃ©. "Orthogonal bandlet basesforgeometric images approximation." Communications on Pure and Applied Mathematics 61, no. 9 (2008): 1173-1212.
Le Pennec, Erwan, and Stephane Mallat. "Bandelet image approximation and compression." Multiscale Modeling & Simulation 4, no. 3 (2005): 992-1039.
Qu, Xiaobo, Jingwen Yan, Guofu Xie, Ziqian Zhu, and Bengang Chen. "A novel image fusion algorithm based on bandelet transform." Chinese Optics Letters 5, no. 10 (2007): 569-572.
Moore, Ryan, Soundararajan Ezekiel, and Erik Blasch. "Denoising one-dimensional signals with curvelets and contourlets." In NAECON 2014-IEEE National Aerospace and Electronics Conference, pp. 189-194. IEEE, 2014.
McLaughlin, Michael J., En-Ui Lin, Erik Blasch, Adnan BÃºbalo, Maria Cornacchia, Mark Alford, and Millicent Thomas. "Multi-resolution deblurring." In 2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1-6. IEEE, 2014.
Yang, Ronggen, and Mingwu Ren. "Wavelet denoising using principal component analysis." Expert Systems with Applications 38, no. 1 (2011): 1073-1076.
Gnanadurai, D., V. Sadasivam, J. Paul Tiburtius Nishandh, L. Muthukumaran, and C. Annamalai. "Undecimated double density wavelet transform based speckle reduction in SAR images." Computers & Electrical Engineering 35, no. 1 (2009): 209-217.
Papageorgiou, Constantine, and Tomaso Poggio. "A trainable system for object detection." International Journal of Computer Vision 38, no. 1 (2000): 15-33.
Kingsbury, Nick. "The dual-tree complex wavelet transform: a new efficient tool for image restoration and enhancement." In Signal Processing Conference (EUSIPCO 1998), 9th European, pp. 1-4. IEEE, 1998.
Liu, Chao-Chun, and Dao-Qing Dai. "Face recognition using dual-tree complex wavelet features." IEEE Transactions on Image Processing 18, no. 11 (2009): 2593-2599.
Wang, Xiang-Yang, and Zhong-Kai Fu. "A wavelet-based image denoising using least squares support vector machine." Engineering Applications of Artificial Intelligence 23, no. 6 (2010): 862-871.
Ezekiel, Soundararajan, Kyle Harrity, Erik Blasch, and Adnan Bubalo. "No-reference blur metric using double-density and dual-tree two-dimensional wavelet transformation." In NAECON 2014-IEEE National Aerospace and Electronics Conference, pp. 109-114. IEEE, 2014.
McLaughlin, Michael J., Samuel Grieggs, Soundararajan Ezekiel, Michael H. Ferris, Erik Blasch, Mark Alford, Maria Cornacchia, and Adnan Bubalo. "Bandelet denoising in image processing." In 2015 National Aerospace and Electronics Conference (NAECON), pp. 35-40. IEEE, 2015.
Giansiracusa, Michael, Adam Lutz, Soundararajan Ezekiel, Mark Alford, Erik Blasch, Adnan Bubalo, and Millicent Thomas. "Multi-focus and multi-modal fusion: a study of multi-resolution transforms." In SPIE Defense+ Security, pp. 98410I-98410I. International Society for Optics and Photonics, 2016.
Ferris, Michael H., Michael McLaughlin, Samuel Grieggs, Soundararajan Ezekiel, Erik Blasch, Mark Alford, Maria Cornacchia, and Adnan Bubalo. "Using ROC curves and AUC to evaluate performance of no-reference image fusion metrics." In 2015 National Aerospace and Electronics Conference (NAECON), pp. 27-34. IEEE, 2015.
Liu, Zheng, Erik Blasch, Zhiyun Xue, Jiying Zhao, Robert Laganiere, and Wei Wu. "Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision: a comparative study." IEEE transactions on pattern analysis and machine intelligence 34, no. 1 (2012): 94-109.
Candes,Emmanuel Jean. "Ridgelets: theory and applications." PhD diss., Stanford University, 1998.
Starck, Jean-Luc, Emmanuel J. CandÃ¨s, and David L. Donoho. "The curvelet transform for image denoising." IEEE Transactions on image processing 11, no. 6 (2002): 670-684.
Tsai, Wei-shi. "Project White Paper-Contourlet Transforms for Feature Detection." (2008).
Sveinsson, Johannes R., and Jon Atli Benediktsson. "Combined wavelet and contourlet denoising of SAR images." In IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium, vol. 3, pp. III-1150. IEEE, 2008.
Xingmei, Li, Yan Guoping, and Chen Liang. "A new method of image denoise using contourlet transform." In Environmental Science and Information Application Technology, 2009. ESIAT 2009. International Conference on, vol. 2, pp. 390-393. IEEE, 2009.
Harrity, Kyle, Soundararajan Ezekiel, Michael Ferris, Maria Cornacchia, and Erik Blasch. "No-reference multi-scale blur metric." In NAECON 2014-IEEE National Aerospace and Electronics Conference, pp. 103-108. IEEE, 2014.
Tzeng, Jack, Chun-Chen Liu, and Truong Q. Nguyen. "Contourlet domain multiband deblurring based on color correlation for fluid lens cameras." IEEE Transactions on Image Processing 19, no. 10 (2010): 2659-2668.
Choi, Yoonsuk, Ershad Sharifahmadian, and Shahram Latifi. "Performance analysis of contourlet-based hyperspectral image fusion methods."International Journal on Information Theory 2, no. 1 (2013): 1-14.
Liu, Zheng, and Erik Blasch. "Statistical analysis of the performance assessment results for pixel-level image fusion." In Information Fusion (FUSION), 2014 17th International Conference on, pp. 1-8. IEEE, 2014.
Messer, Neal, Soundararajan Ezekiel, Michael H. Ferris, Erik Blasch, Mark Alfor, Adnan Bubalo, and Maria Cornacchia. â€œROC Curve Analysis for Validating Objective Image Fusion Metrics.â€ 2015 Applied Image Patter Recognition Workshop (AIPR), IEEE, 2015.
Giansiracusa, Michael, Adam Lutz, Soundararajan Ezekiel, Mark Alford, Erik Blasch, Adnan Bubalo, and Millicent Thomas. â€œBandelet-based image fusion: a comparative study for multi-focus images.â€ 2016 Geospatial Informatics, Fusion, and Video Analytics VI, SPIE Defense + Security Conference, IEEE, 2016
Lutz, Adam, Kendrick Grace, Neal Messer, Soundararajan Ezekiel, Erik Blasch, Mark Alford, Adnan Bubalo, and Maria Cornacchia. â€œBandelet Transformation based Image Registration.â€2015 Applied Image Patter Recognition Workshop (AIPR), IEEE, 2015.
Lutz, Adam, Michael Giansiracusa, Neal Messer, Soundararajan Ezekiel, Erik Blasch, and Mark Alford. â€œOptimal multi-focus contourlet-based image fusion algorithm selection.â€2016 Geospatial Informatics, Fusion, and Video Analytics VI, SPIE Defense + Security Conference, IEEE, 2016
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