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Image Fusion using Variational Mode Decomposition
Background/Objectives: This paper introduced an image fusion algorithm based on Variational Mode Decomposition (VMD). Methods/Statistical Analysis: Image fusion is one of the image enhancement methods which results the image with better quality derived from a set of degraded images. Fused image contains more information than input images and it is efficient for visual perception and computer vision applications. This paper proposed an image fusion technique based on VMD for multi focus images. VMD has been a recently introduced non-recursive decomposition method, which decomposes the image into separate spectral bands called Intrinsic Mode Function (IMF) or modes. The modes are generated with respect to the associated central frequencies and they are band limited. Findings: A fusion rule based on weighing scheme is performed at the decomposition level for increasing the features by decreasing the mutual information. The reconstruction of the IMFs results the final fused image. The performance analysis of the proposed fusion method is experimented using standard objective quality metrics. The efficiency of the proposed method is determined by comparing the method with some state of the art methods. Application/Improvements: The image fusion using VMD is applicable to multi-resolution, multi model multi-sensor images.
Fusion Rule, Image Fusion, Image Quality Metrics, 2D-Variational Mode Decomposition, Variational Mode Decomposition.
- Mitchell HB. Image fusion theories techniques and applications. Springer; 2010 Mar .
- Shivsubramani K. Development of image fusion techniques and measurement methods to assess the quality of the fusion. 2008 Jul.
- Sruthy S, Latha P, Ajeesh PS. Image fusion technique using DT-CWT. Automation, Computing, Communication, Control and Compressed Sensing. IEEE International Multi-Conference; 2013 Mar. p. 160–4.
- Deepak KS, Parsai MP. Different image fusion techniques – A critical review. International Journal of Modern Engineering Research. 2012 Sep; 2(5):4298–301.
- Shivsubramani K, Soman KP. Implementation and comparative study of image fusion algorithms. International Journal of Computer Applications. 2010 Nov; 9(2):25–35.
- Rani K, Reecha S. Study of different image fusion algorithm. International Journal of Emerging Technology and Advanced Engineering. 2013 May; 3(5):288–91.
- Hatte VS, Shingate. A review of image fusion methods. International Journal of Engineering Sciences and Research Technology; 2014 Jun.
- Dragomiretskiy K, Zosso D. Variational Mode Decomposition. IEEE Transaction on Signal Processing. 2014 Feb; 62(3):531–44.
- Savic S. Multifocus image fusion based on empirical mode decomposition. ERK'2011; 2011 Dec. p. 91–4.
- Naidu VPS, Raol JR. Pixel-level image fusion using wavelets and principal component analysis. Defence Science Journal. 2008 May; 58(3):338–52.
- Wencheng W, Faliang C. A multi-focus image fusion method based on Laplacian pyramid. Journal of Computers. 2011 Jan; 6(12):2559–66.
- Deshmukh M, Udhav B. Image fusion and image quality assessment of fused images. International Journal of Image Processing. 2010 Dec; 4(5):484–508.
- Qu Z, Zhang J, Feng Z. Image fusion algorithm based on two-dimensional Discrete Wavelet Transform and Spatial Frequency. IEEE Fifth International Conference on Frontier of Computer Science and Technology (FCST); 2010 Aug. p. 537–40.
- Naidu VPS. Discrete cosine transform based image fusion technique. Journal of Communication, Navigation and Signal Processing. 2012 Jan; 1(1):35–45.
- Hariharan H, Andrei G, Mongi AA, Andreas K. Image fusion and enhancement via empirical mode decomposition. Journal of Pattern Recognition Research. 2006 Jan; 1(1):16–32.
- Looney D, Danilo PM. Multiscale image fusion using complex extensions of EMD. IEEE Transaction on Signal Processing. 2009 Apr; 57(4):1626–30.
- Tania S. Image fusion: Algorithms and applications. 6th ed. Academic Press is an imprint of Elsevier; 2011.
- Jagalingam P, Arkal VH. A review of quality metrics for fused image. Aquatic Procedia; 2015 Dec. p. 133–42.
- Dragomiretskiy K, Zosso D. Two-dimensional Variational Mode Decomposition. Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer International Publishing; 2015 Jan. p. 197–208.
- Silpa K, Aruna MS. Comparison of image quality metrics. International Journal of Engineering Research and Technology. ESRSA Publications; 2012 Jun; 1(4).
- Klonus S, Manfred E. Performance of evaluation methods in image fusion. IEEE 12th International Conference, on Information Fusion; 2009 Jul. p. 1409–16.
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