Abstract

Since the energy of the incident light is constant, the spatial and spectral resolution can hardly be improved without scarifying the other with the spectral imaging method of a pushbroom scanner. Thus, a new spectral imaging method is proposed to obtain a high-resolution (HR) spectral image with a low-resolution detector array. The method, namely coded dispersion, by which compressive measurement is achieved, improves light collection efficiency, and then a high-quality reconstructed HR spectral image is obtained with fewer sensors. The simulation result shows that with prior knowledge of scenes available, the proposed method also offers a new way to acquire an HR spectral image while the density of detector array is constrained by battery, capacity, transmission bandwidth, and cost.

© 2013 Optical Society of America

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  1. K. Wu, R. Niu, and Y. Wang, “Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery,” Opt. Eng. 50, 126201 (2011).
    [CrossRef]
  2. Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
    [CrossRef]
  3. F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine subpixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE. Trans. Image Process. 21, 53–66 (2012).
    [CrossRef]
  4. H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
    [CrossRef]
  5. J. Chen, J. Nunez-Yanez, and A. Achim, “Video super-resolution using generalized Gaussian Markov random fields,” IEEE Signal Process. Lett. 19, 63–66 (2012).
    [CrossRef]
  6. M. Duarte, M. Davenport, D. Takbar, J. Laska, T. Sun, K. F. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25, 83–91 (2008).
    [CrossRef]
  7. R. F. Marcia and R. M. Willett, “Compressive coded aperture superresolution image reconstruction,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2008), pp. 833–836.
  8. A. Wagadarikar, R. John, and R. Willett, “Single disperser design for compressive, single-snapshot spectral imaging,” Proc. SPIE 6714, 67140A (2007).
    [CrossRef]
  9. D. Kittle, K. Choi, A. Wagadarikar, and D. J. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt. 49, 6824–6833 (2010).
    [CrossRef]
  10. H. Arguello and G. Arce, “Code aperture design for compressive spectral imaging,” in Proceedings of the 18th European Signal Processing Conference (European Association for Signal Processing (EURASIP), 2010), pp. 1434–1438.
  11. A. Ashok, P. K. Baheti, and M. A. Neifeld, “Projective imager design with task-specific information,” in Proceedings of Frontiers in Optics (Optical Society of America, 2007), paper FThQ4.
  12. L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.
  13. G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
    [CrossRef]
  14. G. Shi, D. Liu, and D. Gao, “High-resolution computational spectral imaging of remote sensing based on coded sensing,” Spacecraft Recovery Remote Sensing 32, 60–66 (2011) (in Chinese).
  15. J. Mairal, F. Bach, and J. Ponce, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. 11, 19–60 (2010).
  16. M. Aharon, M. Elad, and A. Bruckstein, “k-SVD: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54, 4311–4322(2006).
    [CrossRef]
  17. E. J. Candés and J. Romberg, “Quantitative robust uncertainty principles and optimally sparse decompositions,” Found. Comput. Math. 6, 227–254 (2006).
  18. E. J. Candés, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
    [CrossRef]

2012 (3)

F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine subpixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE. Trans. Image Process. 21, 53–66 (2012).
[CrossRef]

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

J. Chen, J. Nunez-Yanez, and A. Achim, “Video super-resolution using generalized Gaussian Markov random fields,” IEEE Signal Process. Lett. 19, 63–66 (2012).
[CrossRef]

2011 (4)

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

G. Shi, D. Liu, and D. Gao, “High-resolution computational spectral imaging of remote sensing based on coded sensing,” Spacecraft Recovery Remote Sensing 32, 60–66 (2011) (in Chinese).

K. Wu, R. Niu, and Y. Wang, “Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery,” Opt. Eng. 50, 126201 (2011).
[CrossRef]

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

2010 (2)

D. Kittle, K. Choi, A. Wagadarikar, and D. J. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt. 49, 6824–6833 (2010).
[CrossRef]

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

2008 (1)

M. Duarte, M. Davenport, D. Takbar, J. Laska, T. Sun, K. F. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25, 83–91 (2008).
[CrossRef]

2007 (1)

A. Wagadarikar, R. John, and R. Willett, “Single disperser design for compressive, single-snapshot spectral imaging,” Proc. SPIE 6714, 67140A (2007).
[CrossRef]

2006 (3)

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

E. J. Candés and J. Romberg, “Quantitative robust uncertainty principles and optimally sparse decompositions,” Found. Comput. Math. 6, 227–254 (2006).

E. J. Candés, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
[CrossRef]

Achim, A.

J. Chen, J. Nunez-Yanez, and A. Achim, “Video super-resolution using generalized Gaussian Markov random fields,” IEEE Signal Process. Lett. 19, 63–66 (2012).
[CrossRef]

Aharon, M.

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

Arce, G.

H. Arguello and G. Arce, “Code aperture design for compressive spectral imaging,” in Proceedings of the 18th European Signal Processing Conference (European Association for Signal Processing (EURASIP), 2010), pp. 1434–1438.

Arguello, H.

H. Arguello and G. Arce, “Code aperture design for compressive spectral imaging,” in Proceedings of the 18th European Signal Processing Conference (European Association for Signal Processing (EURASIP), 2010), pp. 1434–1438.

Ashok, A.

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Projective imager design with task-specific information,” in Proceedings of Frontiers in Optics (Optical Society of America, 2007), paper FThQ4.

Bach, F.

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

Baheti, P. K.

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Projective imager design with task-specific information,” in Proceedings of Frontiers in Optics (Optical Society of America, 2007), paper FThQ4.

Baraniuk, R.

M. Duarte, M. Davenport, D. Takbar, J. Laska, T. Sun, K. F. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25, 83–91 (2008).
[CrossRef]

Bibet, A.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.

Brady, D. J.

Bruckstein, A.

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

Candés, E. J.

E. J. Candés and J. Romberg, “Quantitative robust uncertainty principles and optimally sparse decompositions,” Found. Comput. Math. 6, 227–254 (2006).

E. J. Candés, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
[CrossRef]

Chen, J.

J. Chen, J. Nunez-Yanez, and A. Achim, “Video super-resolution using generalized Gaussian Markov random fields,” IEEE Signal Process. Lett. 19, 63–66 (2012).
[CrossRef]

Chen, X.

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

Cheng, Y.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Choi, K.

Davenport, M.

M. Duarte, M. Davenport, D. Takbar, J. Laska, T. Sun, K. F. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25, 83–91 (2008).
[CrossRef]

Duarte, M.

M. Duarte, M. Davenport, D. Takbar, J. Laska, T. Sun, K. F. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25, 83–91 (2008).
[CrossRef]

Elad, M.

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

Gao, D.

G. Shi, D. Liu, and D. Gao, “High-resolution computational spectral imaging of remote sensing based on coded sensing,” Spacecraft Recovery Remote Sensing 32, 60–66 (2011) (in Chinese).

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

Jacques, L.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.

John, R.

A. Wagadarikar, R. John, and R. Willett, “Single disperser design for compressive, single-snapshot spectral imaging,” Proc. SPIE 6714, 67140A (2007).
[CrossRef]

Kelly, K. F.

M. Duarte, M. Davenport, D. Takbar, J. Laska, T. Sun, K. F. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25, 83–91 (2008).
[CrossRef]

Kittle, D.

Laska, J.

M. Duarte, M. Davenport, D. Takbar, J. Laska, T. Sun, K. F. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25, 83–91 (2008).
[CrossRef]

Leblebici, Y.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.

Liao, Q.

F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine subpixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE. Trans. Image Process. 21, 53–66 (2012).
[CrossRef]

Liu, D.

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

G. Shi, D. Liu, and D. Gao, “High-resolution computational spectral imaging of remote sensing based on coded sensing,” Spacecraft Recovery Remote Sensing 32, 60–66 (2011) (in Chinese).

Mairal, J.

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

Majidzadeh, V.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.

Marcia, R. F.

R. F. Marcia and R. M. Willett, “Compressive coded aperture superresolution image reconstruction,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2008), pp. 833–836.

Neifeld, M. A.

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Projective imager design with task-specific information,” in Proceedings of Frontiers in Optics (Optical Society of America, 2007), paper FThQ4.

Niu, R.

K. Wu, R. Niu, and Y. Wang, “Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery,” Opt. Eng. 50, 126201 (2011).
[CrossRef]

Nunez-Yanez, J.

J. Chen, J. Nunez-Yanez, and A. Achim, “Video super-resolution using generalized Gaussian Markov random fields,” IEEE Signal Process. Lett. 19, 63–66 (2012).
[CrossRef]

Pan, Q.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Ponce, J.

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

Romberg, J.

E. J. Candés and J. Romberg, “Quantitative robust uncertainty principles and optimally sparse decompositions,” Found. Comput. Math. 6, 227–254 (2006).

E. J. Candés, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
[CrossRef]

Schmid, A.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.

Shi, G.

G. Shi, D. Liu, and D. Gao, “High-resolution computational spectral imaging of remote sensing based on coded sensing,” Spacecraft Recovery Remote Sensing 32, 60–66 (2011) (in Chinese).

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

Song, L.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Song, X.

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

Su, H.

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

Sun, T.

M. Duarte, M. Davenport, D. Takbar, J. Laska, T. Sun, K. F. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25, 83–91 (2008).
[CrossRef]

Takbar, D.

M. Duarte, M. Davenport, D. Takbar, J. Laska, T. Sun, K. F. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25, 83–91 (2008).
[CrossRef]

Tang, L.

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

Tao, T.

E. J. Candés, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
[CrossRef]

Tretter, D.

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

Vandergheynst, P.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.

Wagadarikar, A.

D. Kittle, K. Choi, A. Wagadarikar, and D. J. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt. 49, 6824–6833 (2010).
[CrossRef]

A. Wagadarikar, R. John, and R. Willett, “Single disperser design for compressive, single-snapshot spectral imaging,” Proc. SPIE 6714, 67140A (2007).
[CrossRef]

Wang, Y.

K. Wu, R. Niu, and Y. Wang, “Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery,” Opt. Eng. 50, 126201 (2011).
[CrossRef]

Willett, R.

A. Wagadarikar, R. John, and R. Willett, “Single disperser design for compressive, single-snapshot spectral imaging,” Proc. SPIE 6714, 67140A (2007).
[CrossRef]

Willett, R. M.

R. F. Marcia and R. M. Willett, “Compressive coded aperture superresolution image reconstruction,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2008), pp. 833–836.

Wu, K.

K. Wu, R. Niu, and Y. Wang, “Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery,” Opt. Eng. 50, 126201 (2011).
[CrossRef]

Wu, Y.

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

Xie, X.

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

Yang, J.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Yang, W.

F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine subpixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE. Trans. Image Process. 21, 53–66 (2012).
[CrossRef]

Zhang, Q.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Zhao, Y.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Zhou, F.

F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine subpixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE. Trans. Image Process. 21, 53–66 (2012).
[CrossRef]

Zhou, J.

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

Appl. Opt. (1)

EURASIP J. Adv. Signal Process. (1)

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Found. Comput. Math. (1)

E. J. Candés and J. Romberg, “Quantitative robust uncertainty principles and optimally sparse decompositions,” Found. Comput. Math. 6, 227–254 (2006).

IEEE Signal Process. Lett. (1)

J. Chen, J. Nunez-Yanez, and A. Achim, “Video super-resolution using generalized Gaussian Markov random fields,” IEEE Signal Process. Lett. 19, 63–66 (2012).
[CrossRef]

IEEE Signal Process. Mag. (1)

M. Duarte, M. Davenport, D. Takbar, J. Laska, T. Sun, K. F. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25, 83–91 (2008).
[CrossRef]

IEEE Trans. Image Process. (2)

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

IEEE Trans. Inf. Theory (1)

E. J. Candés, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
[CrossRef]

IEEE Trans. Signal Process. (1)

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

IEEE. Trans. Image Process. (1)

F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine subpixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE. Trans. Image Process. 21, 53–66 (2012).
[CrossRef]

J. Mach. Learn. Res. (1)

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

Opt. Eng. (1)

K. Wu, R. Niu, and Y. Wang, “Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery,” Opt. Eng. 50, 126201 (2011).
[CrossRef]

Proc. SPIE (1)

A. Wagadarikar, R. John, and R. Willett, “Single disperser design for compressive, single-snapshot spectral imaging,” Proc. SPIE 6714, 67140A (2007).
[CrossRef]

Spacecraft Recovery Remote Sensing (1)

G. Shi, D. Liu, and D. Gao, “High-resolution computational spectral imaging of remote sensing based on coded sensing,” Spacecraft Recovery Remote Sensing 32, 60–66 (2011) (in Chinese).

Other (4)

R. F. Marcia and R. M. Willett, “Compressive coded aperture superresolution image reconstruction,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2008), pp. 833–836.

H. Arguello and G. Arce, “Code aperture design for compressive spectral imaging,” in Proceedings of the 18th European Signal Processing Conference (European Association for Signal Processing (EURASIP), 2010), pp. 1434–1438.

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Projective imager design with task-specific information,” in Proceedings of Frontiers in Optics (Optical Society of America, 2007), paper FThQ4.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.

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