Adaptive regularized scheme for remote sensing image fusion
Sizhang TANG, Chaomin SHEN, Guixu ZHANG
Adaptive regularized scheme for remote sensing image fusion
We propose an adaptive regularized algorithm for remote sensing image fusion based on variational methods. In the algorithm, we integrate the inputs using a “grey world” assumption to achieve visual uniformity. We propose a fusion operator that can automatically select the total variation (TV)–L1 term for edges and L2-terms for non-edges. To implement our algorithm, we use the steepest descent method to solve the corresponding Euler–Lagrange equation. Experimental results show that the proposed algorithm achieves remarkable results.
remote sensing image fusion / adaptive regulariser / variational method / steepest descent method
[1] |
Agrawal D, Singhai J (2010). Multi-focus image fusion using modified pulse coupled neural network for improved image quality. IET Image Processing, 4(6): 443–451
CrossRef
Google scholar
|
[2] |
Aubert G, Kornprobst P (2009). Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations. Applied Mathematical Sciences. Berlin: Springer-Verlag
|
[3] |
Bertalmio M, Caselles V, Provenzi E, Rizzi A (2007). Perceptual color correction through variational techniques. IEEE Trans Image Process, 16(4): 1058–1072
CrossRef
Google scholar
|
[4] |
Brelstaff G J, Párraga A, Troscianko T, Carr D (1995). Hyper spectral camera system: acquisition and analysis. In: Satellite Remote Sensing II. International Society for Optics and Photonics: 150–159
|
[5] |
Buchsbaum G (1980). A spatial processor model for object color perception. J Franklin Inst, 310(1): 1–26
CrossRef
Google scholar
|
[6] |
Chai Y, Li H, Zhang X (2012). Multi-focus image fusion based on features contrast of multi-scale products in non-subsampled contourlet transform domain. Optik-International Journal for Light and Electron Optics, 123(7): 569–581
CrossRef
Google scholar
|
[7] |
Chan T F, Esedoglu S (2005). Aspects of total variation regularized l1function approximation. SIAM J Appl Math, 65(5): 1817–1837
CrossRef
Google scholar
|
[8] |
Chan T, Shen J, Vese L (2003). Variational PDE models in image processing. Not Am Math Soc, 50(1): 14–26
|
[9] |
Cvejic N, Seppanen T, Godsill S (2009). A no reference image fusion metric based on the regional importance measure. IEEE Journal Of Selected Topics In Signal Processing, 3(2): 212–221
CrossRef
Google scholar
|
[10] |
Ellmauthaler A, Pagliari C L, Da Silva E A (2013). Multi-scale image fusion using the undecimated wavelet transform with spectral factorization and nonorthogonal filter banks. IEEE Trans Image Process, 22(3): 1005–1017
CrossRef
Google scholar
|
[11] |
El-taweel G S, Helmy A K (2013). Image fusion scheme based on modified dual pulse coupled neural network. IET Image Processing, 7(5): 407–414
|
[12] |
Fang F, Li F, Zhang G, Shen C (2013). A variational method for multisource remote-sensing image fusion. Int J Remote Sens, 34(7): 2470–2486
CrossRef
Google scholar
|
[13] |
Gao G R, Lu P X, Feng D Z (2013). Multi-focus image fusion based on non-sub-sampled shear-let transform. IET Image Processing, 7(6): 633–639
CrossRef
Google scholar
|
[15] |
Hinterberger W, Scherzer O (2006). Variational methods on the space of functions of bounded hessian for convexification and de-noising. Computing, 76(1–2): 109–133
CrossRef
Google scholar
|
[16] |
James A, Dasarathy B (2014). Medical image fusion: a survey of the state of the art. Inf Fusion, 19: 4–19
CrossRef
Google scholar
|
[17] |
Kim Y, Lee C, Han D, Kim Y, Kim Y (2011). Improved additive-wavelet image fusion. IEEE Transactions on Geoscience and Remote Sensing Letters, 8(2): 263–267
CrossRef
Google scholar
|
[18] |
Kluckner S, Pock T, Bischof H (2010). Exploiting redundancy for aerial image fusion using convex optimization. Proceedings of the 32nd DAGM conference on Pattern Recognition, Darmstadt, Germany
|
[19] |
Kong W, Lei Y, Lei Y, Lu S (2011). Image fusion technique based on non-sub-sampled contour-let transform and adaptive unit-fast-linking pulse-coupled neural network. IET Image Processing, 5(2): 113– 121
CrossRef
Google scholar
|
[20] |
Kumar M, Dass S (2009). A total variation-based algorithm for pixel-level image fusion. IEEE Trans Image Process, 18(9): 2137–2143
CrossRef
Google scholar
|
[21] |
Li X, He M, Roux M (2010). Multi-focus image fusion based on redundant wavelet transform. IET Image Processing, 4(4): 283–293
CrossRef
Google scholar
|
[22] |
Liu G, Shen Y (2012). Ultrasonic image fusion using compressed sensing. Electron Lett, 48(19): 1182–1184
CrossRef
Google scholar
|
[24] |
Liu X, Zhou Y, Wang J (2014). Image fusion based on shearlet transform and regional features. AEU, Int J Electron Commun, 68(6): 471– 477
CrossRef
Google scholar
|
[25] |
Lustig M, Donoho D, Pauly J M (2007). Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med, 58(6): 1182–1195
CrossRef
Google scholar
|
[26] |
Lysaker M, Lundervold A, Tai X C (2003). Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Trans Image Process, 12(12): 1579–1590
CrossRef
Google scholar
|
[27] |
Mahbubur Rahman S M, Omair Ahmad M, Swamy M N S (2010). Contrast-based fusion of noisy images using discrete wavelet transform. IET Image Processing, 4(5): 374–384
CrossRef
Google scholar
|
[29] |
Nikolova M (2004). A variational approach to remove outliers and impulse noise. J Math Imaging Vis, 20(1–2): 99–120
CrossRef
Google scholar
|
[30] |
Papafitsoros K, Schönlieb C B (2014). A combined first and second order variational approach for image reconstruction. J Math Imaging Vis, 48(2): 308–338
CrossRef
Google scholar
|
[31] |
Petrovic V (2004). Subjective image fusion evaluation data. Imaging Science Biomedical Engineering. University of Manchester:1–9
|
[33] |
Piella G (2009). Image fusion for enhanced visualization: a variational approach. Int J Comput Vis, 83(1): 1–11
CrossRef
Google scholar
|
[35] |
Pock T, Zebedin L, Bischof H (2011). TGV-fusion. In: Calude C S, Rozenberg A G, Salomaa A, eds. Maurer Festschrift. LNCS, 6570: 245–258
|
[36] |
Pouteau R, Stoll B, Chabrier S (2010). Multi-source SVM fusion for environmental monitoring in marquesas archipelago. In: Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, 2719–2722
|
[38] |
Rudin L I, Osher S, Fatemi E (1992). Non-linear total variation based noise removal algorithms. Physica D, 60(1): 259–268
CrossRef
Google scholar
|
[39] |
Sapiro G (2001). Geometric Partial Differential Equations and Image Analysis. Cambridge University Press
|
[40] |
Schowengerdt R A (2006). Remote sensing: Models and Methods for Image Processing (3rd Edition). Academic Press
|
[41] |
Socolinsky D A (2000). A variational Approach to Image Fusion. Dissertation for PhD degree. The Johns Hopkins University, April 2000
|
[42] |
Socolinsky D A, Wolff L B (2002). Multispectral image visualization through first-order fusion. IEEE Trans Image Process, 11(8): 923–931
CrossRef
Google scholar
|
[43] |
Stathaki (2008). Image Fusion: Algorithms and Applications. Amsterdam: Elsevier
|
[44] |
Tafti P D, Stalder A F, Delgado-Gonzalo R, Unser M (2011). Variational enhancement and de-noising of flow field images. 8th IEEE International symposium on biomedical imaging: from Nano to Macro: 1061–1064
|
[45] |
Tikhonov A N (1943). On the stability of inverse problems. Dokl Akad Nauk SSSR, 39(5): 195–198
|
[46] |
Tran M P, Peteri R, Bergounioux M (2012). De-noising 3D medical images using a second order variational model and wavelet shrinkage. In: Image Analysis and Recognition. Berlin: Springer, 138–145
|
[48] |
Wang C, Ye Z F (2007). Perceptual contrast-based image fusion: a variational approach. Acta Automatica Sinica, 33(2): 132–137
CrossRef
Google scholar
|
[48] |
Wang L, Li B, Tian L (2014). Multi-modal medical volumetric data fusion using 3D discrete shearlet transform and global-to-local rule. IEEE Trans Biomed Eng, 61(1): 197–206
CrossRef
Google scholar
|
[49] |
Wang W W, Shui P L, Feng X C (2008). Variational models for fusion and de-noising of multi-focus images. IEEE Signal Process Lett, 15: 65–68
CrossRef
Google scholar
|
[51] |
Wang Z, Ziou D, Armenakis C, Li D, Li Q (2005). A comparative analysis of image fusion methods. IEEE Trans Geosci Rem Sens, 43(6): 1391–1402
CrossRef
Google scholar
|
[53] |
Yang B, Li S (2010). Multi-focus image fusion and restoration with sparse representation. IEEE Trans Instrum Meas, 59(4): 884–892
CrossRef
Google scholar
|
[54] |
Yang L, Guo B, Ni W (2008). Multimodality medical image fusion based on multi-scale geometric analysis of contourlet transform. Neurocomputing, 72(1–3): 203–211
CrossRef
Google scholar
|
[55] |
Yang Z Z, Yang Z (2013). Novel multi-focus image fusion and reconstruction framework based on compressed sensing. IET Image Processing, 7(9): 837–847
CrossRef
Google scholar
|
[56] |
Yuan J, Miles B, Shi J, Garvin G, Tai X C and A. Fenster A (2012). Efficient convex optimization approaches to variational image fusion. UCLA Tech. Report CAM-11-86
|
[57] |
Zhang X, Chan T F (2010). Wavelet inpainting by nonlocal total variation. Inverse Problems and Imaging, 4(1): 191–210
CrossRef
Google scholar
|
[58] |
Zheng S, Shi W, Liu J, Zhu G, Tian J (2007). Multisource image fusion method using support value transform. IEEE Trans Image Process, 16: 1
|
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