영상개선 방식을 적용한 개선된 다 해상도 영상융합 프레임워크
An Improved Multi-resolution image fusion framework using image enhancement technique
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초록
This paper represents a novel framework for multi-scale image fusion. Multi-scale Kalman Smoothing (MKS) algorithm with quad-tree structure can provide a powerful multi-resolution image fusion scheme by employing Markov property. In general, such approach provides outstanding image fusion performance in terms of accuracy and efficiency, however, quad-tree based method is often limited to be applied in certain applications due to its stair-like covariance structure, resulting in unrealistic blocky artifacts at the fusion result where finest scale data are void or missed. To mitigate this structural artifact, in this paper, a new scheme of multi-scale fusion framework is proposed. By employing Super Resolution (SR) technique on MKS algorithm, fine resolved measurement is generated and blended through the tree structure such that missed detail information at data missing region in fine scale image is properly inferred and the blocky artifact can be successfully suppressed at fusion result. Simulation results show that the proposed method provides significantly improved fusion results in the senses of both Root Mean Square Error (RMSE) performance and visual improvement over conventional MKS algorithm.
Abstract
This paper represents a novel framework for multi-scale image fusion. Multi-scale Kalman Smoothing (MKS) algorithm with quad-tree structure can provide a powerful multi-resolution image fusion scheme by employing Markov property. In general, such approach provides outstanding image fusion performance in terms of accuracy and efficiency, however, quad-tree based method is often limited to be applied in certain applications due to its stair-like covariance structure, resulting in unrealistic blocky artifacts at the fusion result where finest scale data are void or missed. To mitigate this structural artifact, in this paper, a new scheme of multi-scale fusion framework is proposed. By employing Super Resolution (SR) technique on MKS algorithm, fine resolved measurement is generated and blended through the tree structure such that missed detail information at data missing region in fine scale image is properly inferred and the blocky artifact can be successfully suppressed at fusion result. Simulation results show that the proposed method provides significantly improved fusion results in the senses of both Root Mean Square Error (RMSE) performance and visual improvement over conventional MKS algorithm.
참고문헌 (16)
- G. Pajares, "A wavelet-based image fusion tutorial", Pattern Recognition, 37(Issue 9), pp.1855~1872 (2004)
- M. M. Daniel, "A multiresolution methodology for signal-level fusion and data assimilation with applications to remote sensing", Proc. IEEE, 85, pp.164~180 (1997)
- W. Irving, "A Theory for Multiscale Stochastic Realization", 33rd Conference on Decision and Control, 1, pp.655~662 (1994)
- H. E. Rauch, "Maximum likelihood estimates of linear dynamic systems", AIAA Journal, 3(8), pp.1445~1450 (1965)
- K. Chou, "Multiscale systems, Kalman filters, and Riccati equations", IEEE Transactions on Automatic Control, 39, pp.479~492 (1994)
- S.M Kay, "Fundamentals of Statistical Signal Processing: Estimation Theory" (1993), Prentice Hall
- L. Yue, "A locally adaptive L1−L2 norm for multi-frame super-resolution of images with mixed noise and outliers", Signal Processing, 105, pp.156~174 (2014)
- Q. Zhou, "The recent advancement in digital terrain analysis and modeling", International Journal of Geographical Information Science, 27(7), pp.1269~1271 (2013)
- M.R. Luettgen, "Multiscale Representations of Markov Random Fields", IEEE Trans. Signal Processing, 41, pp.3377~3395 (1993)
- A. S. Willsky, "Multiresolution Markov models for signal and image processing", Proceedings of the IEEE, 90(8), pp.1396~1458 (2002)
- W. Fieguth, "Multiscale Stochastic Processing of Topex", Poseidon Oceanogrphic Altimetry, 2000, pp.9, 456~468
- K. Chou, "Multiscale systems, Kalman filters, and Riccati equations", IEEE Transactions on Automatic Control, 39(3), pp.479~492 (1994)
- K.C. Slatton, "Fusing interferometric radar and laser altimeter data to estimate surface topography and vegetation heights", IEEE Transactions on Geoscience and Remote Sensing, 39, pp.2470~2482 (2001)
- H. Zebker, "The TOPSAR interferometric radar topographic mapping instrument", IEEE Transactions on Geoscience and Remote Sensing, 30, pp.933~940 (1992)
- H. Jhee, "Efficient observational updating for fusion of incomplete image data", Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 4, pp.2807~2810 (2005)
- S. Todorovic, "Interpretation of complex scenes using dynamic tree-structure Bayesian networks", Computer Vision and Image Understanding, 106(1), pp.71~84 (2007)