X. Gong, B. Lai, and Z. Xiang, “A L0 sparse analysis prior for blind poissonian image deconvolution,” Opt. Express 22, 370–375 (2014).

[Crossref]

H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral image restoration using low-rank matrix recovery,” IEEE Trans. Geosci. Remote Sens. 52, 4729–4743 (2014).

[Crossref]

X. Liu, S. Bourennane, and C. Fossati, “Denoising of hyperspectral images using the PARAFAC model and statistical performance analysis,” IEEE Trans. Geosci. Remote Sens. 50, 3717–3724 (2012).

[Crossref]

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

[Crossref]

T. Skauli, “An upper-bound metric for characterizing spectral and spatial coregistration errors in spectral imaging,” Opt. Express 20, 918–933 (2012).

[Crossref]
[PubMed]

R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 1–24 (2009).

[Crossref]

S. Osher and T. Goldstein, “The Split Bregman method for L1 regularized problems,” SIAM J. Imaging Sci. 2, 323–343 (2009).

[Crossref]

D. Letexier and S. Bourennane, “Noise removal from hyperspectral images by multidimensional filtering,” IEEE Trans. Geosci. Remote Sens. 46, 2061–2069 (2008).

[Crossref]

T. Le, R. Chartrand, and T. J. Asaki, “A variational approach to reconstructing images corrupted by Poisson noise,” J. Math. Imaging Vis. 27, 257–263 (2007).

[Crossref]

J. Martín-Herrero, “Anisotropic diffusion in the hypercube,” IEEE Trans. Geosci. Remote Sens. 45, 1386–1398 (2007).

[Crossref]

H. Othman and S.-E. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens. 44, 397–408 (2006).

[Crossref]

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]
[PubMed]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60, 259–268 (1992).

[Crossref]

T. Le, R. Chartrand, and T. J. Asaki, “A variational approach to reconstructing images corrupted by Poisson noise,” J. Math. Imaging Vis. 27, 257–263 (2007).

[Crossref]

R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 1–24 (2009).

[Crossref]

R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 1–24 (2009).

[Crossref]

E. L. Dereniak and G. D. Boreman, Infrared Detectors and Systems (Wiley, 1996).

X. Liu, S. Bourennane, and C. Fossati, “Denoising of hyperspectral images using the PARAFAC model and statistical performance analysis,” IEEE Trans. Geosci. Remote Sens. 50, 3717–3724 (2012).

[Crossref]

D. Letexier and S. Bourennane, “Noise removal from hyperspectral images by multidimensional filtering,” IEEE Trans. Geosci. Remote Sens. 46, 2061–2069 (2008).

[Crossref]

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]
[PubMed]

T. Le, R. Chartrand, and T. J. Asaki, “A variational approach to reconstructing images corrupted by Poisson noise,” J. Math. Imaging Vis. 27, 257–263 (2007).

[Crossref]

F. Deger, A. Mansouri, M. Pedersen, J. Y. Hardeberg, and Y. Voisin, “A variational approach for denoising hyperspectral images corrupted by Poisson distributed noise,” in Image Signal Process (Springer, 2014), pp. 106–114.

[Crossref]

E. L. Dereniak and G. D. Boreman, Infrared Detectors and Systems (Wiley, 1996).

M. D. Fairchild and G. M. Johnson, “Metacow: a public-domain, high-extended-dynamic-range, spectral test target for imaging system analysis and simulation,” in “Color Imaging Conf.”, (IS&T, 2004), pp. 239–245.

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60, 259–268 (1992).

[Crossref]

X. Liu, S. Bourennane, and C. Fossati, “Denoising of hyperspectral images using the PARAFAC model and statistical performance analysis,” IEEE Trans. Geosci. Remote Sens. 50, 3717–3724 (2012).

[Crossref]

R. Shrestha, R. Pillay, S. George, and J. Y. Hardeberg, “Quality evaluation in spectral imaging–quality factors and metrics,” JAIC-Journal of the International Colour Association12 (2014).

P. Getreuer, “Rudin-Osher-Fatemi total variation denoising using Split Bregman,” Image Process. Line (2012).

S. Osher and T. Goldstein, “The Split Bregman method for L1 regularized problems,” SIAM J. Imaging Sci. 2, 323–343 (2009).

[Crossref]

X. Gong, B. Lai, and Z. Xiang, “A L0 sparse analysis prior for blind poissonian image deconvolution,” Opt. Express 22, 370–375 (2014).

[Crossref]

R. Shrestha, R. Pillay, S. George, and J. Y. Hardeberg, “Quality evaluation in spectral imaging–quality factors and metrics,” JAIC-Journal of the International Colour Association12 (2014).

F. Deger, A. Mansouri, M. Pedersen, J. Y. Hardeberg, and Y. Voisin, “A variational approach for denoising hyperspectral images corrupted by Poisson distributed noise,” in Image Signal Process (Springer, 2014), pp. 106–114.

[Crossref]

H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral image restoration using low-rank matrix recovery,” IEEE Trans. Geosci. Remote Sens. 52, 4729–4743 (2014).

[Crossref]

M. D. Fairchild and G. M. Johnson, “Metacow: a public-domain, high-extended-dynamic-range, spectral test target for imaging system analysis and simulation,” in “Color Imaging Conf.”, (IS&T, 2004), pp. 239–245.

X. Gong, B. Lai, and Z. Xiang, “A L0 sparse analysis prior for blind poissonian image deconvolution,” Opt. Express 22, 370–375 (2014).

[Crossref]

T. Le, R. Chartrand, and T. J. Asaki, “A variational approach to reconstructing images corrupted by Poisson noise,” J. Math. Imaging Vis. 27, 257–263 (2007).

[Crossref]

D. Letexier and S. Bourennane, “Noise removal from hyperspectral images by multidimensional filtering,” IEEE Trans. Geosci. Remote Sens. 46, 2061–2069 (2008).

[Crossref]

H. Li and L. Zhang, “A hybrid automatic endmember extraction algorithm based on a local window,” IEEE Trans. Geosci. Remote Sens. 49, 4223–4238 (2011).

[Crossref]

X. Liu, S. Bourennane, and C. Fossati, “Denoising of hyperspectral images using the PARAFAC model and statistical performance analysis,” IEEE Trans. Geosci. Remote Sens. 50, 3717–3724 (2012).

[Crossref]

F. Deger, A. Mansouri, M. Pedersen, J. Y. Hardeberg, and Y. Voisin, “A variational approach for denoising hyperspectral images corrupted by Poisson distributed noise,” in Image Signal Process (Springer, 2014), pp. 106–114.

[Crossref]

J. Martín-Herrero, “Anisotropic diffusion in the hypercube,” IEEE Trans. Geosci. Remote Sens. 45, 1386–1398 (2007).

[Crossref]

S. Osher and T. Goldstein, “The Split Bregman method for L1 regularized problems,” SIAM J. Imaging Sci. 2, 323–343 (2009).

[Crossref]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60, 259–268 (1992).

[Crossref]

H. Othman and S.-E. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens. 44, 397–408 (2006).

[Crossref]

F. Deger, A. Mansouri, M. Pedersen, J. Y. Hardeberg, and Y. Voisin, “A variational approach for denoising hyperspectral images corrupted by Poisson distributed noise,” in Image Signal Process (Springer, 2014), pp. 106–114.

[Crossref]

R. Shrestha, R. Pillay, S. George, and J. Y. Hardeberg, “Quality evaluation in spectral imaging–quality factors and metrics,” JAIC-Journal of the International Colour Association12 (2014).

H. Othman and S.-E. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens. 44, 397–408 (2006).

[Crossref]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60, 259–268 (1992).

[Crossref]

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]
[PubMed]

H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral image restoration using low-rank matrix recovery,” IEEE Trans. Geosci. Remote Sens. 52, 4729–4743 (2014).

[Crossref]

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

[Crossref]

R. Shrestha, R. Pillay, S. George, and J. Y. Hardeberg, “Quality evaluation in spectral imaging–quality factors and metrics,” JAIC-Journal of the International Colour Association12 (2014).

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]
[PubMed]

F. Deger, A. Mansouri, M. Pedersen, J. Y. Hardeberg, and Y. Voisin, “A variational approach for denoising hyperspectral images corrupted by Poisson distributed noise,” in Image Signal Process (Springer, 2014), pp. 106–114.

[Crossref]

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]
[PubMed]

X. Gong, B. Lai, and Z. Xiang, “A L0 sparse analysis prior for blind poissonian image deconvolution,” Opt. Express 22, 370–375 (2014).

[Crossref]

J. Yang and Y. Zhao, “Poisson-Gaussian mixed noise removing for hyperspectral image via spatial-spectral structure similarity,” in “32nd Chinese Control Conf.” (Xi’an, 2013), pp. 3715–3720.

H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral image restoration using low-rank matrix recovery,” IEEE Trans. Geosci. Remote Sens. 52, 4729–4743 (2014).

[Crossref]

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

[Crossref]

R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 1–24 (2009).

[Crossref]

R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 1–24 (2009).

[Crossref]

H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral image restoration using low-rank matrix recovery,” IEEE Trans. Geosci. Remote Sens. 52, 4729–4743 (2014).

[Crossref]

H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral image restoration using low-rank matrix recovery,” IEEE Trans. Geosci. Remote Sens. 52, 4729–4743 (2014).

[Crossref]

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

[Crossref]

H. Li and L. Zhang, “A hybrid automatic endmember extraction algorithm based on a local window,” IEEE Trans. Geosci. Remote Sens. 49, 4223–4238 (2011).

[Crossref]

J. Yang and Y. Zhao, “Poisson-Gaussian mixed noise removing for hyperspectral image via spatial-spectral structure similarity,” in “32nd Chinese Control Conf.” (Xi’an, 2013), pp. 3715–3720.

H. Li and L. Zhang, “A hybrid automatic endmember extraction algorithm based on a local window,” IEEE Trans. Geosci. Remote Sens. 49, 4223–4238 (2011).

[Crossref]

X. Liu, S. Bourennane, and C. Fossati, “Denoising of hyperspectral images using the PARAFAC model and statistical performance analysis,” IEEE Trans. Geosci. Remote Sens. 50, 3717–3724 (2012).

[Crossref]

H. Othman and S.-E. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens. 44, 397–408 (2006).

[Crossref]

J. Martín-Herrero, “Anisotropic diffusion in the hypercube,” IEEE Trans. Geosci. Remote Sens. 45, 1386–1398 (2007).

[Crossref]

D. Letexier and S. Bourennane, “Noise removal from hyperspectral images by multidimensional filtering,” IEEE Trans. Geosci. Remote Sens. 46, 2061–2069 (2008).

[Crossref]

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

[Crossref]

H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral image restoration using low-rank matrix recovery,” IEEE Trans. Geosci. Remote Sens. 52, 4729–4743 (2014).

[Crossref]

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]
[PubMed]

R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 1–24 (2009).

[Crossref]

T. Le, R. Chartrand, and T. J. Asaki, “A variational approach to reconstructing images corrupted by Poisson noise,” J. Math. Imaging Vis. 27, 257–263 (2007).

[Crossref]

T. Skauli, “Sensor noise informed representation of hyperspectral data, with benefits for image storage and processing,” Opt. Express 19, 13031–13046 (2011).

[Crossref]
[PubMed]

T. Skauli, “An upper-bound metric for characterizing spectral and spatial coregistration errors in spectral imaging,” Opt. Express 20, 918–933 (2012).

[Crossref]
[PubMed]

X. Gong, B. Lai, and Z. Xiang, “A L0 sparse analysis prior for blind poissonian image deconvolution,” Opt. Express 22, 370–375 (2014).

[Crossref]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60, 259–268 (1992).

[Crossref]

S. Osher and T. Goldstein, “The Split Bregman method for L1 regularized problems,” SIAM J. Imaging Sci. 2, 323–343 (2009).

[Crossref]

E. L. Dereniak and G. D. Boreman, Infrared Detectors and Systems (Wiley, 1996).

M. D. Fairchild and G. M. Johnson, “Metacow: a public-domain, high-extended-dynamic-range, spectral test target for imaging system analysis and simulation,” in “Color Imaging Conf.”, (IS&T, 2004), pp. 239–245.

J. Padfield, “Library of illumination spectral power distributions,” http://research.ng-london.org.uk/scientific/spd/ . Accessed: 2014-12-10.

R. Shrestha, R. Pillay, S. George, and J. Y. Hardeberg, “Quality evaluation in spectral imaging–quality factors and metrics,” JAIC-Journal of the International Colour Association12 (2014).

F. Deger, A. Mansouri, M. Pedersen, J. Y. Hardeberg, and Y. Voisin, “A variational approach for denoising hyperspectral images corrupted by Poisson distributed noise,” in Image Signal Process (Springer, 2014), pp. 106–114.

[Crossref]

(HySpex / Norsk Elektro Optikk AS), “Imaging spectrometer (user manual),” Tech. Rep. (2013).

J. Yang and Y. Zhao, “Poisson-Gaussian mixed noise removing for hyperspectral image via spatial-spectral structure similarity,” in “32nd Chinese Control Conf.” (Xi’an, 2013), pp. 3715–3720.

P. Getreuer, “Rudin-Osher-Fatemi total variation denoising using Split Bregman,” Image Process. Line (2012).