Abstract

Multiband polarization epithelial tissue imaging is an effective tool to measure tissue’s birefringence and structure for quantitative pathology analysis. To discriminate the pathology accurately, high-resolution multiband polarization images are essential. But it is difficult to acquire high-resolution polarization images because of the limitations of imaging systems. The polarization image calculation process can be regarded as image fusion with fixed rules, and multiband polarization images are intrinsically sparse. In this paper, we propose a novel high-resolution multiband polarization image calculation method by utilizing the sparse representation and image fusion method. The multiband images are first represented in the sparse domain and we further introduce total-variation-regularization terms into the sparse representation framework. Then, polarization parameter images are calculated by simultaneous fusion and reconstruction. Higher quality multiband polarization images can be obtained through additional regularization constraint in the fusion process. Extensive experiments validate that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both peak signal-to-noise-ratio and visual perception.

© 2012 Optical Society of America

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  1. R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).
    [CrossRef]
  2. Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for anomaly epithelial tissue detection,” in Sequence and Genome Analysis: Methods and Applications (CreateSpace, 2011), pp. 297–330.
  3. Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for pathological analysis of skin,” Appl. Opt. 48, D236–D246 (2009).
    [CrossRef]
  4. S. L. Jacques, J. C. Ramella-Roman, and K. Lee, “Imaging skin pathology with polarized light,” J. Biomed. Opt. 7, 329–340 (2002).
    [CrossRef]
  5. R. Perkins and V. Gruev, “Signal-to-noise analysis of Stokes parameters in division of focal plane polarimeters,” Opt. Express 18, 25815–25824 (2010).
    [CrossRef]
  6. A. Bénière, F. Goudail, M. Alouini, and D. Dolfi, “Degree of polarization estimation in the presence of nonuniform illumination and additive Gaussian noise,” J. Opt. Soc. Am. A 25, 919–929 (2008).
    [CrossRef]
  7. S. B. Howell, Handbook of CCD Astronomy (Cambridge University, 2006).
  8. Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).
    [CrossRef]
  9. M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).
    [CrossRef]
  10. L. Capobianco, A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image,” in IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2007), pp. 5158–5161.
  11. F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, “Remote sensing image fusion using the curvelet transform,” Information Fusion 8, 143–156 (2007).
    [CrossRef]
  12. W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).
    [CrossRef]
  13. J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).
    [CrossRef]
  14. T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent development in total variation image restoration,” in Mathematical Models of Computer VisionN. Paragios, Y. Chen, and O. Faugeras, eds. (Springer Verlag, 2005), pp. 17–30.
  15. B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature 381, 607–609 (1996).
    [CrossRef]
  16. Y. Zhao, Q. Pan, and H. Zhang, “New polarization imaging method based on spatially adaptive wavelet image fusion,” Opt. Eng. 45, 123202 (2006).
    [CrossRef]
  17. M. Aharon, M. Elad, and A. Bruckstein, “The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representations,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
    [CrossRef]
  18. H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Conference on Neural Information Processing Systems (MIT Press, 2007), pp. 801–808.
  19. J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Machine Learning Res. 11, 19–60 (2010).
  20. 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]
  21. L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
    [CrossRef]
  22. A. Buades, B. Coll, and J. M. Morel, “A non local algorithm for image denoising,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.
  23. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE 6812, 681207 (2008).
    [CrossRef]

2011 (3)

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).
[CrossRef]

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).
[CrossRef]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
[CrossRef]

2010 (3)

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Machine Learning Res. 11, 19–60 (2010).

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).
[CrossRef]

R. Perkins and V. Gruev, “Signal-to-noise analysis of Stokes parameters in division of focal plane polarimeters,” Opt. Express 18, 25815–25824 (2010).
[CrossRef]

2009 (1)

2008 (2)

A. Bénière, F. Goudail, M. Alouini, and D. Dolfi, “Degree of polarization estimation in the presence of nonuniform illumination and additive Gaussian noise,” J. Opt. Soc. Am. A 25, 919–929 (2008).
[CrossRef]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE 6812, 681207 (2008).
[CrossRef]

2007 (1)

F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, “Remote sensing image fusion using the curvelet transform,” Information Fusion 8, 143–156 (2007).
[CrossRef]

2006 (2)

Y. Zhao, Q. Pan, and H. Zhang, “New polarization imaging method based on spatially adaptive wavelet image fusion,” Opt. Eng. 45, 123202 (2006).
[CrossRef]

M. Aharon, M. Elad, and A. Bruckstein, “The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representations,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
[CrossRef]

2005 (1)

M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).
[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]

2002 (1)

S. L. Jacques, J. C. Ramella-Roman, and K. Lee, “Imaging skin pathology with polarized light,” J. Biomed. Opt. 7, 329–340 (2002).
[CrossRef]

2001 (1)

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).
[CrossRef]

1996 (1)

B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature 381, 607–609 (1996).
[CrossRef]

Aharon, M.

M. Aharon, M. Elad, and A. Bruckstein, “The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representations,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
[CrossRef]

Alouini, M.

Alparone, L.

F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, “Remote sensing image fusion using the curvelet transform,” Information Fusion 8, 143–156 (2007).
[CrossRef]

L. Capobianco, A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image,” in IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2007), pp. 5158–5161.

Bach, F.

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Machine Learning Res. 11, 19–60 (2010).

Backman, V.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).
[CrossRef]

Badizadegan, K.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).
[CrossRef]

Baronti, S.

F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, “Remote sensing image fusion using the curvelet transform,” Information Fusion 8, 143–156 (2007).
[CrossRef]

L. Capobianco, A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image,” in IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2007), pp. 5158–5161.

Battle, A.

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Conference on Neural Information Processing Systems (MIT Press, 2007), pp. 801–808.

Bénière, A.

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]

Bruckstein, A.

M. Aharon, M. Elad, and A. Bruckstein, “The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representations,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
[CrossRef]

Buades, A.

A. Buades, B. Coll, and J. M. Morel, “A non local algorithm for image denoising,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.

Capobianco, L.

L. Capobianco, A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image,” in IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2007), pp. 5158–5161.

Chan, T.

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent development in total variation image restoration,” in Mathematical Models of Computer VisionN. Paragios, Y. Chen, and O. Faugeras, eds. (Springer Verlag, 2005), pp. 17–30.

Cheng., Y.

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).
[CrossRef]

Choi, M.

M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).
[CrossRef]

Coll, B.

A. Buades, B. Coll, and J. M. Morel, “A non local algorithm for image denoising,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.

Dabov, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE 6812, 681207 (2008).
[CrossRef]

Dasari, R. R.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).
[CrossRef]

Dolfi, D.

Dong, W.

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).
[CrossRef]

Egiazarian, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE 6812, 681207 (2008).
[CrossRef]

Elad, M.

M. Aharon, M. Elad, and A. Bruckstein, “The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representations,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
[CrossRef]

Esedoglu, S.

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent development in total variation image restoration,” in Mathematical Models of Computer VisionN. Paragios, Y. Chen, and O. Faugeras, eds. (Springer Verlag, 2005), pp. 17–30.

Feld, M. S.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).
[CrossRef]

Field, D. J.

B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature 381, 607–609 (1996).
[CrossRef]

Foi, A.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE 6812, 681207 (2008).
[CrossRef]

Garzelli, A.

F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, “Remote sensing image fusion using the curvelet transform,” Information Fusion 8, 143–156 (2007).
[CrossRef]

L. Capobianco, A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image,” in IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2007), pp. 5158–5161.

Georgakoudi, I.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).
[CrossRef]

Goudail, F.

Gruev, V.

Gurjar, R. S.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).
[CrossRef]

Hong, O.

M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).
[CrossRef]

Howell, S. B.

S. B. Howell, Handbook of CCD Astronomy (Cambridge University, 2006).

Huang, T. S.

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).
[CrossRef]

Itzkan, I.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).
[CrossRef]

Jacques, S. L.

S. L. Jacques, J. C. Ramella-Roman, and K. Lee, “Imaging skin pathology with polarized light,” J. Biomed. Opt. 7, 329–340 (2002).
[CrossRef]

Katkovnik, V.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE 6812, 681207 (2008).
[CrossRef]

Kim, R.

M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).
[CrossRef]

Lee, H.

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Conference on Neural Information Processing Systems (MIT Press, 2007), pp. 801–808.

Lee, K.

S. L. Jacques, J. C. Ramella-Roman, and K. Lee, “Imaging skin pathology with polarized light,” J. Biomed. Opt. 7, 329–340 (2002).
[CrossRef]

Ma, Y.

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).
[CrossRef]

Mairal, J.

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Machine Learning Res. 11, 19–60 (2010).

Morel, J. M.

A. Buades, B. Coll, and J. M. Morel, “A non local algorithm for image denoising,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.

Mou, X.

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
[CrossRef]

Nam, M.

M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).
[CrossRef]

Nencini, F.

F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, “Remote sensing image fusion using the curvelet transform,” Information Fusion 8, 143–156 (2007).
[CrossRef]

L. Capobianco, A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image,” in IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2007), pp. 5158–5161.

Ng, A. Y.

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Conference on Neural Information Processing Systems (MIT Press, 2007), pp. 801–808.

Olshausen, B. A.

B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature 381, 607–609 (1996).
[CrossRef]

Pan, Q.

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).
[CrossRef]

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for pathological analysis of skin,” Appl. Opt. 48, D236–D246 (2009).
[CrossRef]

Y. Zhao, Q. Pan, and H. Zhang, “New polarization imaging method based on spatially adaptive wavelet image fusion,” Opt. Eng. 45, 123202 (2006).
[CrossRef]

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for anomaly epithelial tissue detection,” in Sequence and Genome Analysis: Methods and Applications (CreateSpace, 2011), pp. 297–330.

Park, F.

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent development in total variation image restoration,” in Mathematical Models of Computer VisionN. Paragios, Y. Chen, and O. Faugeras, eds. (Springer Verlag, 2005), pp. 17–30.

Perelman, L. T.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).
[CrossRef]

Perkins, R.

Ponce, J.

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Machine Learning Res. 11, 19–60 (2010).

Raina, R.

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Conference on Neural Information Processing Systems (MIT Press, 2007), pp. 801–808.

Ramella-Roman, J. C.

S. L. Jacques, J. C. Ramella-Roman, and K. Lee, “Imaging skin pathology with polarized light,” J. Biomed. Opt. 7, 329–340 (2002).
[CrossRef]

Sapiro, G.

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Machine Learning Res. 11, 19–60 (2010).

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]

Shi, G.

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).
[CrossRef]

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]

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]

Wright, J.

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).
[CrossRef]

Wu, X.

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).
[CrossRef]

Yang, J.

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).
[CrossRef]

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).
[CrossRef]

Yip, A.

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent development in total variation image restoration,” in Mathematical Models of Computer VisionN. Paragios, Y. Chen, and O. Faugeras, eds. (Springer Verlag, 2005), pp. 17–30.

Zhang, D.

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
[CrossRef]

Zhang, H.

Y. Zhao, Q. Pan, and H. Zhang, “New polarization imaging method based on spatially adaptive wavelet image fusion,” Opt. Eng. 45, 123202 (2006).
[CrossRef]

Zhang, L.

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).
[CrossRef]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
[CrossRef]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
[CrossRef]

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for pathological analysis of skin,” Appl. Opt. 48, D236–D246 (2009).
[CrossRef]

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for anomaly epithelial tissue detection,” in Sequence and Genome Analysis: Methods and Applications (CreateSpace, 2011), pp. 297–330.

Zhang, Q.

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).
[CrossRef]

Zhao, Y.

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).
[CrossRef]

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for pathological analysis of skin,” Appl. Opt. 48, D236–D246 (2009).
[CrossRef]

Y. Zhao, Q. Pan, and H. Zhang, “New polarization imaging method based on spatially adaptive wavelet image fusion,” Opt. Eng. 45, 123202 (2006).
[CrossRef]

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for anomaly epithelial tissue detection,” in Sequence and Genome Analysis: Methods and Applications (CreateSpace, 2011), pp. 297–330.

Appl. Opt. (1)

EURASIP J. Adv. Signal Process. (1)

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).
[CrossRef]

IEEE Geosci. Remote Sens. Lett. (1)

M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).
[CrossRef]

IEEE Trans. Image Process. (4)

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).
[CrossRef]

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).
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Figures (12)

Fig. 1.
Fig. 1.

High-quality epithelial tissue multiband polarization imaging system.

Fig. 2.
Fig. 2.

Set of high-quality epithelial tissue images used for training dictionaries. (a)–(d) Four example images in the epithelial tissue image set.

Fig. 3.
Fig. 3.

Set of high-quality multiband polarization epithelial tissue images used for training dictionaries. (a)–(d) Four example I(0) images in the epithelial tissue image set, which are acquired at 580 nm.

Fig. 4.
Fig. 4.

Dictionaries trained by two different image sets: (a) dictionaries trained by the color image set and (b) dictionaries trained by color images.

Fig. 5.
Fig. 5.

Reconstruction results for a multiband polarization image S0 at 500 nm. (a) Original S0 image, (b) denoising by nonlocal means (PSNR=21.41, SSIM=0.342273), (c) denoising by BM3D (PSNR=22.70, SSIM=0.53451), and (d) reconstruction result by Algorithm 2 (PSNR=23.96, SSIM=0.78336).

Fig. 6.
Fig. 6.

Reconstruction results for a multiband polarization image S0 at 580 nm. (a) Original S0 image, (b) denoising by nonlocal means (PSNR=22.41, SSIM=0.342278), (c) denoising by BM3D (PSNR=22.90, SSIM=0.53457), and (d) reconstruction result by Algorithm 2 (PSNR=23.66, SSIM=0.78334).

Fig. 7.
Fig. 7.

Reconstruction results for a multiband polarization image S1 at 580 nm. (a) Original S1 image, (b) denoising by nonlocal means (PSNR=23.10, SSIM=0.367861), (c) denoising by BM3D (PSNR=23.64, SSIM=0.43464), and (d) reconstruction result by Algorithm 2 (PSNR=25.96, SSIM=0.8011).

Fig. 8.
Fig. 8.

Reconstruction results for a multiband polarization image S2 at 580 nm. (a) Original S2 image, (b) denoising by nonlocal means (PSNR=24.10, SSIM=0.342270), (c) denoising by nonlocal means (PSNR=24.90, SSIM=0.447910), and (d) superresolution by Algorithm 2 (PSNR=25.81, SSIM=0.8773).

Fig. 9.
Fig. 9.

Reconstruction results for a multiband polarization image S0 at 550 nm. (a) Low-resolution image (downsampled in the spatial domain by a factor of 3), (b) bicubic interpolation (FSIM=0.583), (c) superresolution by the TV model (FSIM=0.611), (d) superresolution by the proposed algorithm using the first dictionary (FSIM=0.682), and (e) superresolution by the proposed algorithm using the second dictionary (FSIM=0.683).

Fig. 10.
Fig. 10.

Reconstruction results for a multiband polarization image S1 at 550 nm. (a) Low-resolution image (downsampled in the spatial domain by a factor of 3), (b) bicubic interpolation (FSIM=0.583), (c) superresolution by the TV model (FSIM=0.601), (d) superresolution by the proposed algorithm using the first dictionary (FSIM=0.660), and (e) superresolution by the proposed algorithm using the second dictionary (FSIM=0.661).

Fig. 11.
Fig. 11.

Reconstruction results for a multiband polarization image S2 at 550 nm. (a) Low-resolution image (downsampled in the spatial domain by a factor of 3), (b) bicubic interpolation (FSIM=0.602), (c) superresolution by the TV model (FSIM=0.621), (d) superresolution by the proposed algorithm using the first dictionary (FSIM=0.691), and (e) superresolution by the proposed algorithm using the second dictionary (FSIM=0.690).

Fig. 12.
Fig. 12.

DoLP images at 550 nm calculated by the original and reconstructed data. (a) DoLP image calculated by original high-quality data and (b) DoLP image calculated by reconstructed high-quality data.

Equations (18)

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S0=I(0)+I(90),
S1=I(0)I(90),
S2=I(45)+I(135),
DoLP=S12+S22S0.
SignalNoise=NumobjNumobj+npix(NumS+NumD+NumR2),
argminΦ,α{xΦα22+λα1},
Y=WHX+υ,
X^=argminYWX22.
YWΦα+υ,
α^=argminα{YWΦα22+λα1}.
α^=argminα{YWΦα22+λα1+γTV(Φα)}.
I(0)=Φα0,
I(90)=Φα90.
S0=I(0)+I(90)=Φ(α0+α90).
{α^0,α^90}=argmin{α0,α90}{S0WΦ(α0+α90)22+λα01+βα901+γTV(Φ(α0+α90))}.
PSNR=20log(b=1KSpeak,bMSE),
SSIM(x,y)=(2μxTμy+C1)(2σxy+C2)(μxTμx+μyTμy+C1)(σxTσx+σyTσy+C2).
FSIM(x,y)=zΩSL(z)·PCm(z)zΩPCm(z),

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