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Abstract
Effective edge-preserving methods are applied to produce relatively high informative images by merging multi-focus images in image fusion applications. In this paper, anisotropic diffusion filter (ADF)-based image fusion algorithm is proposed. Weight map layers are constructed through image smoothing using an edge-preserving method which is further processed by ADF before applying the fusion rule to obtain the final output. Experimental results are analyzed qualitatively as well as quantitatively and have proved to be more efficient than a few of the existing methods in multiple tests.
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References
Agrawal D, Karar V, Kapur P, Singh GS (2014) Multispectral image fusion for enhancing situation awareness: a review. IETE Tech Rev 13(6):463–470.https://doi.org/10.1080/02564602.2014.968225
Aurich V, Weule J (1995) Non-linear gaussian filters performing edge preserving diffusion. In: Informatik aktuell, Springer, Berlin.https://doi.org/10.1007/978-3-642-79980-8_63
Bhatnagar G, Wu Q, Liu Z (2013) Directive contrast based multimodal medical image fusion in NSCT domain. IEEE Trans Multimedia 15(5):1014–1024.https://doi.org/10.1109/TMM.2013.2244870
Bioucas-Dias JM et al (2012) Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J Sel Top Appl Earth Observ Remote Sens 5(2):354–379.https://doi.org/10.1109/JSTARS.2012.2194696
Choudhury P, Tumblin J (2003) The trilateral filter for high contrast images and meshes. In: Eurographics symposium on rendering, Department of Computer Science, Northwestern University, Evanston, IL, USA
Daneshvar S, Pourghassem H, Danishvar M, Ghassemian H (2011) Combination of feature and pixel level image fusion with feedback retina and IHS. IAENG Int J Comput Sci 38(3):302
GholamHosseini H, Alizad A, Fatemi M (2006) Fusion of vibro—acoustography images and Xray mammography. In: Proceedings of annual international conference, IEEE Engineering in Medicine and Biology Society
James AP, Dasarathy BV (2014) Medical image fusion: a survey of the state of the art. Inf Fusion 19:4–19.https://doi.org/10.1016/j.inffus.2013.12.002
Kim JH, Lee KK, Kim JO (2021) Multi-exposure image fusion through feature decomposition. In: International conference on consumer electronics-Asia, IEEE.https://doi.org/10.1109/ICCE-Asia53811.2021.9642010
Lazhar K, Mignotte M (2021) A new fusion framework for motion segmentation in dynamic scenes. Int J Image Data Fusion 12(2):99–121.https://doi.org/10.1080/19479832.2021.1900408
Li S, Kang X (2012) Fast Multi-exposure image fusion with median filter and recursive filter. IEEE Trans Consum Electron 58(2):626–632.https://doi.org/10.1109/TCE.2012.6227469
Li S, Kang X, Jianwen Hu (2013a) Image fusion with guided filtering. IEEE Trans Image Process 22(7):2864–2875.https://doi.org/10.1109/TIP.2013.2244222
Li S, Kang X, Jianwen Hu, Yang B (2013b) Image matting for fusion of multi-focus images in dynamic scenes. Inf Fusion 14(2):147–162.https://doi.org/10.1016/j.inffus.2011.07.001
Liu Z et al (2012) Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision: a comparative study. IEEE Trans Pattern Anal Mach Intell 34(1):94–109.https://doi.org/10.1109/TPAMI.2011.109
Liu Yu, Liu S, Wang Z (2015) A general framework for image fusion based on multi-scale transform and sparse representation. Inf Fusion 24:147–164.https://doi.org/10.1016/j.inffus.2014.09.004
Meher B, Agrawal S, Panda R, Abraham A (2021) A region based remote sensing image fusion using anisotropic diffusion process. Int J Image Data Fusion.https://doi.org/10.1080/19479832.2021.2019132
Mertens T, Kautz J, Van Reeth F (2007) Exposure fusion. In: Computer graphics and applications, IEEE Xplore.https://doi.org/10.1109/PG.2007.17
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12:629
Savić S (2011) Multifocus image fusion based on empirical mode decomposition. In: Twentieth international electrotechnical and computer science conference, ERK.https://dsp.etfbl.net/mif/
Shaky A, Biswas M, Pal M (2021) Fusion and classification of multi-temporal SAR and optical imagery using convolutional neural network. Int J Image Data Fusion.https://doi.org/10.1080/19479832.2021.2019133
Shen R, Cheng I, Basu A (2013) QoE-based multi-exposure fusion in hierarchical multivariate gaussian CRF. IEEE Trans Image Process 22(6):2469–2478
Tarabalka Y, Fauvel M, Chanusso J, Benediktsson JA (2010) SVM- and MRF-based method for accurate classification of hyperspectral images. IEEE Geosci Remote Sens Lett 7(4):736–740.https://doi.org/10.1109/LGRS.2010.2047711
Tebini S, Seddik H, Braiek EB (2016) An advanced and adaptive mathematical function for an efficient anisotropic image filtering. Comput Math Appl 72:1369–1385
Veganzones MA et al (2016) Hyperspectral super-resolution of locally low rank images from complementary multisource data. IEEE Trans Image Process 25(1):274–288.https://doi.org/10.1109/TIP.2015.2496263
Zhang J (2010) Multi-source remote sensing data fusion: status and trends. Int J Image Data Fusion 1(1):5–24.https://doi.org/10.1080/19479830903561035
Zhang Z, Blum RS (2003) Multisensor image fusion using a region-basedwavelet transform approach. Comput Sci
Zheng S, Shi W-Z, Liu J, Zhu G (2007) Multisource image fusion method using support value transform. IEEE Trans Image Process 16(7):1831–1839.https://doi.org/10.1109/TIP.2007.896687
Zhou Z, Wang B, Li S, Dong M (2016) Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with gaussian and bilateral filters. Inf Fusion 30(1):15–26.https://doi.org/10.1016/j.inffus.2015.11.003
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National Institute of Technology, Tiruchirappalli, 620015, India
G. Tirumala Vasu & P. Palanisamy
Presidency University, Bangalore, India
G. Tirumala Vasu
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Vasu, G.T., Palanisamy, P. Multi-focus image fusion using anisotropic diffusion filter.Soft Comput26, 14029–14040 (2022). https://doi.org/10.1007/s00500-022-07562-2
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