Abstract

Hyperspectral images are often contaminated with stripe noise, which severely degrades the imaging quality and the precision of the subsequent processing. In this paper, a variational model is proposed by employing spectral-spatial adaptive unidirectional variation and a sparse representation. Unlike traditional methods, we exploit the spectral correction and remove stripes in different bands and different regions adaptively, instead of selecting parameters band by band. The regularization strength adapts to the spectrally varying stripe intensities and the spatially varying texture information. Spectral correlation is exploited via dictionary learning in the sparse representation framework to prevent spectral distortion. Moreover, the minimization problem, which contains two unsmooth and inseparable l1-norm terms, is optimized by the split Bregman approach. Experimental results, on datasets from several imaging systems, demonstrate that the proposed method can remove stripe noise effectively and adaptively, as well as preserve original detail information.

© 2016 Optical Society of Korea

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    [Crossref]

2015 (1)

Y.-Q. Zhao and J. Yang, “Hyperspectral image denoising via sparse representation and low-rank constraint,” IEEE Trans. Geosci. Rem. Sens. 53, 296-308 (2015).
[Crossref]

2014 (4)

2013 (4)

2012 (2)

J. Fehrenbach, P. Weiss, and C. Lorenzo, “Variational algorithms to remove stationary noise: Applications to microscopy imaging,” IEEE Trans. Image Process. 21, 4420-4430 (2012).
[Crossref]

Q. Yuan, L. Zhang, and H. Shen, “Hyperspectral image denoising employing a spectral-spatial adaptive total variation model,” IEEE Trans. Geosci. Rem. Sens. 50, 3660-3677 (2012).
[Crossref]

2011 (3)

M. Bouali and S. Ladjal, “Toward optimal destriping of MODIS data using a unidirectional variational model,” IEEE Trans. Geosci. Rem. Sens. 49, 2924-2935 (2011).
[Crossref]

R. Pande-Chhetri and A. Abd-Elrahman, “De-striping hyper-spectral imagery using wavelet transform and adaptive frequency domain filtering,” ISPRS J. Photogramm. Remote Sens. 66, 620-636 (2011).
[Crossref]

E. Vera, P. Meza, and S. Torres, “Total variation approach for adaptive nonuniformity correction in focal-plane arrays,” Opt. Lett. 36, 172-174 (2011).
[Crossref]

2010 (3)

2009 (2)

B. Münch, P. Trtik, F. Marone, and M. Stampanoni, “Stripe and ring artifact removal with combined wavelet-Fourier filtering,” Opt. Express 17, 8567-8591 (2009).
[Crossref]

T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM J. Imag. Sci. 2, 323-343 (2009).
[Crossref]

2008 (1)

X. Bresson and T. F. Chan, “Fast dual minimization of the vectorial total variation norm and applications to color image processing,” Inverse Probl. Imag. 2, 455-484 (2008).
[Crossref]

2007 (3)

2006 (2)

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15, 3736-3745 (2006).
[Crossref]

J. Chen, H. Lin, Y. Shao, and L. Yang, “Oblique striping removal in remote sensing imagery based on wavelet transform,” Int. J. Remote Sens. 27, 1717-1723 (2006).
[Crossref]

2004 (1)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600-612 (2004).
[Crossref]

2003 (1)

J. Chen, Y. Shao, H. Guo, W. Wang, and B. Zhu, “Destriping CMODIS data by power filtering,” IEEE Trans. Geosci. Rem. Sens. 41, 2119-2124 (2003).
[Crossref]

2001 (1)

J. Torres and S. O. Infante, “Wavelet analysis for the elimination of striping noise in satellite images,” Opt. Eng. 40, 1309-1314 (2001).
[Crossref]

1993 (1)

S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397-3415 (1993).
[Crossref]

1989 (1)

M. Weinreb, R. Xie, J. Lienesch, and D. Crosby, “Destriping GOES images by matching empirical distribution functions,” Remote Sens. Environ. 29, 185-195 (1989).
[Crossref]

1979 (1)

B. K. Horn and R. J. Woodham, “Destriping Landsat MSS images by histogram modification,” Comput. Graph. Image Process. 10, 69-83 (1979).
[Crossref]

Abd-Elrahman, A.

R. Pande-Chhetri and A. Abd-Elrahman, “De-striping hyper-spectral imagery using wavelet transform and adaptive frequency domain filtering,” ISPRS J. Photogramm. Remote Sens. 66, 620-636 (2011).
[Crossref]

Aharon, M.

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15, 3736-3745 (2006).
[Crossref]

Anastasio, M. A.

Andersson-Engels, S.

Averbuch, Y.

Y. Y. Schechner and Y. Averbuch, “Regularized image recovery in scattering media,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 1655-1660 (2007).
[Crossref]

Axelsson, J.

Batenburg, K. J.

Bequé, D.

Bioucas-Dias, J.

J. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, and J. Chanussot, “Hyperspectral remote sensing data analysis and future challenges,” IEEE Geosci. Remote Sens. Mag. 1, 6-36 (2013).

Bouali, M.

K. Mikelsons, M. Wang, L. Jiang, and M. Bouali, “Destriping algorithm for improved satellite-derived ocean color product imagery,” Opt. Express 22, 28058-28070 (2014).
[Crossref]

M. Bouali and S. Ladjal, “Toward optimal destriping of MODIS data using a unidirectional variational model,” IEEE Trans. Geosci. Rem. Sens. 49, 2924-2935 (2011).
[Crossref]

Bovik, A. C.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600-612 (2004).
[Crossref]

Bresson, X.

X. Bresson and T. F. Chan, “Fast dual minimization of the vectorial total variation norm and applications to color image processing,” Inverse Probl. Imag. 2, 455-484 (2008).
[Crossref]

Camps-Valls, G.

J. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, and J. Chanussot, “Hyperspectral remote sensing data analysis and future challenges,” IEEE Geosci. Remote Sens. Mag. 1, 6-36 (2013).

Cao, N.

Chan, T. F.

X. Bresson and T. F. Chan, “Fast dual minimization of the vectorial total variation norm and applications to color image processing,” Inverse Probl. Imag. 2, 455-484 (2008).
[Crossref]

Chang, Y.

Y. Chang, L. Yan, H. Fang, and H. Liu, “Simultaneous destriping and denoising for remote sensing images with unidirectional total variation and sparse representation,” IEEE Trans. Geosci. Rem. Sens. 11, 1051-1055 (2014).
[Crossref]

Y. Chang, H. Fang, L. Yan, and H. Liu, “Robust destriping method with unidirectional total variation and framelet regularization,” Opt. Express 21, 23307-23323 (2013).
[Crossref]

Chanussot, J.

J. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, and J. Chanussot, “Hyperspectral remote sensing data analysis and future challenges,” IEEE Geosci. Remote Sens. Mag. 1, 6-36 (2013).

Chen, J.

J. Chen, H. Lin, Y. Shao, and L. Yang, “Oblique striping removal in remote sensing imagery based on wavelet transform,” Int. J. Remote Sens. 27, 1717-1723 (2006).
[Crossref]

J. Chen, Y. Shao, H. Guo, W. Wang, and B. Zhu, “Destriping CMODIS data by power filtering,” IEEE Trans. Geosci. Rem. Sens. 41, 2119-2124 (2003).
[Crossref]

Chen, Q.

Q. Chen, P. Montesinos, Q. S. Sun, P. A. Heng, and D. S. Xia, “Adaptive total variation denoising based on difference curvature,” Image Vis. Comput. 28, 298-306 (2010).
[Crossref]

Clason, C.

Cozzini, C.

Crosby, D.

M. Weinreb, R. Xie, J. Lienesch, and D. Crosby, “Destriping GOES images by matching empirical distribution functions,” Remote Sens. Environ. 29, 185-195 (1989).
[Crossref]

Edic, P. M.

Elad, M.

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15, 3736-3745 (2006).
[Crossref]

Fang, H.

Y. Chang, L. Yan, H. Fang, and H. Liu, “Simultaneous destriping and denoising for remote sensing images with unidirectional total variation and sparse representation,” IEEE Trans. Geosci. Rem. Sens. 11, 1051-1055 (2014).
[Crossref]

Y. Chang, H. Fang, L. Yan, and H. Liu, “Robust destriping method with unidirectional total variation and framelet regularization,” Opt. Express 21, 23307-23323 (2013).
[Crossref]

Fehrenbach, J.

J. Fehrenbach, P. Weiss, and C. Lorenzo, “Variational algorithms to remove stationary noise: Applications to microscopy imaging,” IEEE Trans. Image Process. 21, 4420-4430 (2012).
[Crossref]

Freiberger, M.

Goldstein, T.

T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM J. Imag. Sci. 2, 323-343 (2009).
[Crossref]

Gong, G.

Guo, H.

J. Chen, Y. Shao, H. Guo, W. Wang, and B. Zhu, “Destriping CMODIS data by power filtering,” IEEE Trans. Geosci. Rem. Sens. 41, 2119-2124 (2003).
[Crossref]

Heng, P. A.

Q. Chen, P. Montesinos, Q. S. Sun, P. A. Heng, and D. S. Xia, “Adaptive total variation denoising based on difference curvature,” Image Vis. Comput. 28, 298-306 (2010).
[Crossref]

Horn, B. K.

B. K. Horn and R. J. Woodham, “Destriping Landsat MSS images by histogram modification,” Comput. Graph. Image Process. 10, 69-83 (1979).
[Crossref]

Infante, S. O.

J. Torres and S. O. Infante, “Wavelet analysis for the elimination of striping noise in satellite images,” Opt. Eng. 40, 1309-1314 (2001).
[Crossref]

Jacobs, M.

Jiang, L.

King, A.

Kostenko, A.

Kudielka, G. P.

Ladjal, S.

M. Bouali and S. Ladjal, “Toward optimal destriping of MODIS data using a unidirectional variational model,” IEEE Trans. Geosci. Rem. Sens. 49, 2924-2935 (2011).
[Crossref]

Lienesch, J.

M. Weinreb, R. Xie, J. Lienesch, and D. Crosby, “Destriping GOES images by matching empirical distribution functions,” Remote Sens. Environ. 29, 185-195 (1989).
[Crossref]

Lin, H.

J. Chen, H. Lin, Y. Shao, and L. Yang, “Oblique striping removal in remote sensing imagery based on wavelet transform,” Int. J. Remote Sens. 27, 1717-1723 (2006).
[Crossref]

Liu, H.

Y. Chang, L. Yan, H. Fang, and H. Liu, “Simultaneous destriping and denoising for remote sensing images with unidirectional total variation and sparse representation,” IEEE Trans. Geosci. Rem. Sens. 11, 1051-1055 (2014).
[Crossref]

Y. Chang, H. Fang, L. Yan, and H. Liu, “Robust destriping method with unidirectional total variation and framelet regularization,” Opt. Express 21, 23307-23323 (2013).
[Crossref]

Lorenzo, C.

J. Fehrenbach, P. Weiss, and C. Lorenzo, “Variational algorithms to remove stationary noise: Applications to microscopy imaging,” IEEE Trans. Image Process. 21, 4420-4430 (2012).
[Crossref]

Mahdi, K.

Mallat, S.

S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397-3415 (1993).
[Crossref]

Marone, F.

Meza, P.

Mikelsons, K.

Montesinos, P.

Q. Chen, P. Montesinos, Q. S. Sun, P. A. Heng, and D. S. Xia, “Adaptive total variation denoising based on difference curvature,” Image Vis. Comput. 28, 298-306 (2010).
[Crossref]

Münch, B.

Nasrabadi, N.

J. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, and J. Chanussot, “Hyperspectral remote sensing data analysis and future challenges,” IEEE Geosci. Remote Sens. Mag. 1, 6-36 (2013).

Nehorai, A.

Offerman, S. E.

Osher, S.

T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM J. Imag. Sci. 2, 323-343 (2009).
[Crossref]

Pan, X.

Pande-Chhetri, R.

R. Pande-Chhetri and A. Abd-Elrahman, “De-striping hyper-spectral imagery using wavelet transform and adaptive frequency domain filtering,” ISPRS J. Photogramm. Remote Sens. 66, 620-636 (2011).
[Crossref]

Plaza, A.

J. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, and J. Chanussot, “Hyperspectral remote sensing data analysis and future challenges,” IEEE Geosci. Remote Sens. Mag. 1, 6-36 (2013).

Scharfetter, H.

Schechner, Y. Y.

Y. Y. Schechner and Y. Averbuch, “Regularized image recovery in scattering media,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 1655-1660 (2007).
[Crossref]

Scheunders, P.

J. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, and J. Chanussot, “Hyperspectral remote sensing data analysis and future challenges,” IEEE Geosci. Remote Sens. Mag. 1, 6-36 (2013).

Shao, Y.

J. Chen, H. Lin, Y. Shao, and L. Yang, “Oblique striping removal in remote sensing imagery based on wavelet transform,” Int. J. Remote Sens. 27, 1717-1723 (2006).
[Crossref]

J. Chen, Y. Shao, H. Guo, W. Wang, and B. Zhu, “Destriping CMODIS data by power filtering,” IEEE Trans. Geosci. Rem. Sens. 41, 2119-2124 (2003).
[Crossref]

Sheikh, H. R.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600-612 (2004).
[Crossref]

Shen, H.

Q. Yuan, L. Zhang, and H. Shen, “Hyperspectral image denoising employing a spectral-spatial adaptive total variation model,” IEEE Trans. Geosci. Rem. Sens. 50, 3660-3677 (2012).
[Crossref]

Sidky, E. Y.

Simoncelli, E. P.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600-612 (2004).
[Crossref]

Sperl, J. I.

Stampanoni, M.

Suhonen, H.

Sun, Q. S.

Q. Chen, P. Montesinos, Q. S. Sun, P. A. Heng, and D. S. Xia, “Adaptive total variation denoising based on difference curvature,” Image Vis. Comput. 28, 298-306 (2010).
[Crossref]

Svensson, J.

Torres, J.

J. Torres and S. O. Infante, “Wavelet analysis for the elimination of striping noise in satellite images,” Opt. Eng. 40, 1309-1314 (2001).
[Crossref]

Torres, S.

Trtik, P.

van Vliet, L. J.

Vera, E.

Wang, M.

Wang, W.

J. Chen, Y. Shao, H. Guo, W. Wang, and B. Zhu, “Destriping CMODIS data by power filtering,” IEEE Trans. Geosci. Rem. Sens. 41, 2119-2124 (2003).
[Crossref]

Wang, Z.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600-612 (2004).
[Crossref]

Weinreb, M.

M. Weinreb, R. Xie, J. Lienesch, and D. Crosby, “Destriping GOES images by matching empirical distribution functions,” Remote Sens. Environ. 29, 185-195 (1989).
[Crossref]

Weiss, P.

J. Fehrenbach, P. Weiss, and C. Lorenzo, “Variational algorithms to remove stationary noise: Applications to microscopy imaging,” IEEE Trans. Image Process. 21, 4420-4430 (2012).
[Crossref]

Woodham, R. J.

B. K. Horn and R. J. Woodham, “Destriping Landsat MSS images by histogram modification,” Comput. Graph. Image Process. 10, 69-83 (1979).
[Crossref]

Xia, D. S.

Q. Chen, P. Montesinos, Q. S. Sun, P. A. Heng, and D. S. Xia, “Adaptive total variation denoising based on difference curvature,” Image Vis. Comput. 28, 298-306 (2010).
[Crossref]

Xie, R.

M. Weinreb, R. Xie, J. Lienesch, and D. Crosby, “Destriping GOES images by matching empirical distribution functions,” Remote Sens. Environ. 29, 185-195 (1989).
[Crossref]

Yan, L.

Y. Chang, L. Yan, H. Fang, and H. Liu, “Simultaneous destriping and denoising for remote sensing images with unidirectional total variation and sparse representation,” IEEE Trans. Geosci. Rem. Sens. 11, 1051-1055 (2014).
[Crossref]

Y. Chang, H. Fang, L. Yan, and H. Liu, “Robust destriping method with unidirectional total variation and framelet regularization,” Opt. Express 21, 23307-23323 (2013).
[Crossref]

Yang, J.

Y.-Q. Zhao and J. Yang, “Hyperspectral image denoising via sparse representation and low-rank constraint,” IEEE Trans. Geosci. Rem. Sens. 53, 296-308 (2015).
[Crossref]

Yang, L.

J. Chen, H. Lin, Y. Shao, and L. Yang, “Oblique striping removal in remote sensing imagery based on wavelet transform,” Int. J. Remote Sens. 27, 1717-1723 (2006).
[Crossref]

Yao, M.

Yuan, Q.

Q. Yuan, L. Zhang, and H. Shen, “Hyperspectral image denoising employing a spectral-spatial adaptive total variation model,” IEEE Trans. Geosci. Rem. Sens. 50, 3660-3677 (2012).
[Crossref]

Zhang, H.

Zhang, L.

Q. Yuan, L. Zhang, and H. Shen, “Hyperspectral image denoising employing a spectral-spatial adaptive total variation model,” IEEE Trans. Geosci. Rem. Sens. 50, 3660-3677 (2012).
[Crossref]

Zhang, Z.

S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397-3415 (1993).
[Crossref]

Zhao, Y.-Q.

Y.-Q. Zhao and J. Yang, “Hyperspectral image denoising via sparse representation and low-rank constraint,” IEEE Trans. Geosci. Rem. Sens. 53, 296-308 (2015).
[Crossref]

Zhu, B.

J. Chen, Y. Shao, H. Guo, W. Wang, and B. Zhu, “Destriping CMODIS data by power filtering,” IEEE Trans. Geosci. Rem. Sens. 41, 2119-2124 (2003).
[Crossref]

Appl. Opt. (1)

Comput. Graph. Image Process. (1)

B. K. Horn and R. J. Woodham, “Destriping Landsat MSS images by histogram modification,” Comput. Graph. Image Process. 10, 69-83 (1979).
[Crossref]

IEEE Geosci. Remote Sens. Mag. (1)

J. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, and J. Chanussot, “Hyperspectral remote sensing data analysis and future challenges,” IEEE Geosci. Remote Sens. Mag. 1, 6-36 (2013).

IEEE Trans. Geosci. Rem. Sens. (5)

J. Chen, Y. Shao, H. Guo, W. Wang, and B. Zhu, “Destriping CMODIS data by power filtering,” IEEE Trans. Geosci. Rem. Sens. 41, 2119-2124 (2003).
[Crossref]

M. Bouali and S. Ladjal, “Toward optimal destriping of MODIS data using a unidirectional variational model,” IEEE Trans. Geosci. Rem. Sens. 49, 2924-2935 (2011).
[Crossref]

Q. Yuan, L. Zhang, and H. Shen, “Hyperspectral image denoising employing a spectral-spatial adaptive total variation model,” IEEE Trans. Geosci. Rem. Sens. 50, 3660-3677 (2012).
[Crossref]

Y.-Q. Zhao and J. Yang, “Hyperspectral image denoising via sparse representation and low-rank constraint,” IEEE Trans. Geosci. Rem. Sens. 53, 296-308 (2015).
[Crossref]

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