D. K. Hammond and E. P. Simoncelli, “Image modeling and denoising with orientation-adapted Gaussian scale mixtures,” IEEE Trans. Image Process. 17, 2089-2101 (2008).

[CrossRef]

J. M. Bioucas-Dias and M. A. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16, 2992-3004 (2007).

[CrossRef]

M. A. Figueiredo, J. M. Bioucas-Dias, and R. D. Nowak, “Majorization-minimization algorithms for wavelet-based image restoration,” IEEE Trans. Image Process. 16, 2980-2991 (2007).

[CrossRef]

D. Krishnan, P. Lin, and A. M. Yip, “A primal-dual active-set method for nonnegativity constrained total variation deblurring problems,” IEEE Trans. Image Process. 16, 2766-2777(2007).

[CrossRef]

J. M. Bioucas-Dias, “Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors,” IEEE Trans. Image Process. 15, 937-951 (2006).

[CrossRef]

J. E. Fowler, “The redundant discrete wavelet transform and additive noise,” IEEE Signal Process. Lett. 12, 629-632 (2005).

[CrossRef]

R. C. Puetter, T. R. Gosnell, and A. Yahil, “Digital image reconstruction: deblurring and denoising,” Annu. Rev. Astron. Astrophys. 43, 139-194 (2005).

[CrossRef]

R. Neelamani, H. Choi, and R. Baraniuk, “ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems,” IEEE Trans. Signal Process. 52, 418-433 (2004).

[CrossRef]

A. Jalobeanu, L. Blanc-Féraud, and J. Zerubia, “An adaptive Gaussian model for satellite image deblurring,” IEEE Trans. Image Process. 13, 613-621 (2004).

[CrossRef]

J. Kalifa, S. Mallat, and B. Rouge, “Deconvolution by thresholding in mirror wavelet bases,” IEEE Trans. Image Process. 12, 446-457 (2003).

[CrossRef]

J. Kalifa and S. Mallat, “Thresholding estimators for linear inverse problems and deconvolutions,” Ann. Statist. 31, 58-109 (2003).

M. A. Figueiredo and R. D. Nowak, “An EM algorithm for wavelet-based image restoration,” IEEE Trans. Image Process. 12, 906-916 (2003).

[CrossRef]

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process. 12, 1338-1351(2003).

[CrossRef]

S. K. Patra, N. Mishra, R. Chandrakanth, and R. Ramachandran, “Image quality improvement through MTF compensation: a treatment to high resolution data,” Indian Cartogr. 22, 86-93 (2002).

M. A. Figueiredo and R. D. Nowak, “Wavelet-based image estimation: an empirical Bayes approach using Jeffreys' noninformative prior,” IEEE Trans. Image Process. 10, 1322-1331 (2001).

[CrossRef]

S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Process. 9, 1532-1546 (2000).

[CrossRef]

P. Moulin and J. Liu, “Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors,” IEEE Trans. Inf. Theory 45, 909-919 (1999).

[CrossRef]

J. Kalifa, S. Mallat, and B. Rouge, “Minimax solution of inverse problems and deconvolution by mirror wavelet thresholding,” Proc. SPIE 3813, 42-57 (1999).

[CrossRef]

S. P. Ghael, A. M. Sayeed, and R. G. Baraniuk, “Improved wavelet denoising via empirical Wiener filtering,” Proc. SPIE 3169, 389-399 (1997).

[CrossRef]

I. M. Johnstone and B. W. Silverman, “Wavelet threshold estimators for data with correlated noise,” J. R. Statist. Soc. Ser. B 59, 319-351 (1997).

F. Aghdasi and R. K. Ward, “Reduction of boundary artifacts in image restoration,” IEEE Trans. Image Process. 5, 611-618(1996).

[CrossRef]

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10-12 (1996).

[CrossRef]

D. L. Donoho, “Nonlinear solution of linear inverse problems by wavelet-vaguelette decomposition,” Appl. Comput. Harmon. Anal. 2, 101-126 (1995).

[CrossRef]

J. Fessler and A. Hero, “Penalized maximum-likelihood image reconstruction using space-alternating generalized EM algorithms,” IEEE Trans. Image Process. 4, 1417-1429 (1995).

[CrossRef]

A. D. Hillery and R. T. Chin, “Iterative Wiener filters for image restoration,” IEEE Trans. Signal Process. 39, 1892-1899(1991).

[CrossRef]

M. Y. Zhou, *Deconvolution and Signal Recovery* (National Defence Industry Press, 2004).

F. Aghdasi and R. K. Ward, “Reduction of boundary artifacts in image restoration,” IEEE Trans. Image Process. 5, 611-618(1996).

[CrossRef]

H. Andrews and B. Hunt, *Digital Image Restoration* (Prentice-Hall, 1977).

R. Neelamani, H. Choi, and R. Baraniuk, “ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems,” IEEE Trans. Signal Process. 52, 418-433 (2004).

[CrossRef]

S. P. Ghael, A. M. Sayeed, and R. G. Baraniuk, “Improved wavelet denoising via empirical Wiener filtering,” Proc. SPIE 3169, 389-399 (1997).

[CrossRef]

J. M. Bioucas-Dias and M. A. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16, 2992-3004 (2007).

[CrossRef]

M. A. Figueiredo, J. M. Bioucas-Dias, and R. D. Nowak, “Majorization-minimization algorithms for wavelet-based image restoration,” IEEE Trans. Image Process. 16, 2980-2991 (2007).

[CrossRef]

J. M. Bioucas-Dias, “Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors,” IEEE Trans. Image Process. 15, 937-951 (2006).

[CrossRef]

J. M. Bioucas-Dias, M. A. Figueiredo, and J. P. Oliveira, “Total variation-based image deconvolution: a majorization-minimization approach,” in *2006 IEEE International Conference on Acoustics, Speech and Signal Processing* (IEEE, 2006), pp. 861-864.

A. Jalobeanu, L. Blanc-Féraud, and J. Zerubia, “An adaptive Gaussian model for satellite image deblurring,” IEEE Trans. Image Process. 13, 613-621 (2004).

[CrossRef]

A. Jalobeanu, L. Blanc-Féraud, and J. Zerubia, “Satellite image deconvolution using complex wavelet packets,” INRIA Research Report 3955 (Institut National de Recherche en Informatique et en Automatique, 2000), pp. 7-73.

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10-12 (1996).

[CrossRef]

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent developments in total variation image restoration,” in *Mathematical Models of Computer Vision* (Springer-Verlag, 2005).

S. K. Patra, N. Mishra, R. Chandrakanth, and R. Ramachandran, “Image quality improvement through MTF compensation: a treatment to high resolution data,” Indian Cartogr. 22, 86-93 (2002).

S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Process. 9, 1532-1546 (2000).

[CrossRef]

A. D. Hillery and R. T. Chin, “Iterative Wiener filters for image restoration,” IEEE Trans. Signal Process. 39, 1892-1899(1991).

[CrossRef]

R. Neelamani, H. Choi, and R. Baraniuk, “ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems,” IEEE Trans. Signal Process. 52, 418-433 (2004).

[CrossRef]

T. Choi, “IKONOS satellite on orbit modulation transfer function (MTF) measurement using edge and pulse method,” Ph.D. dissertation (South Dakota State University, 2002), pp. 41-89.

D. L. Donoho, “Nonlinear solution of linear inverse problems by wavelet-vaguelette decomposition,” Appl. Comput. Harmon. Anal. 2, 101-126 (1995).

[CrossRef]

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent developments in total variation image restoration,” in *Mathematical Models of Computer Vision* (Springer-Verlag, 2005).

J. Fessler and A. Hero, “Penalized maximum-likelihood image reconstruction using space-alternating generalized EM algorithms,” IEEE Trans. Image Process. 4, 1417-1429 (1995).

[CrossRef]

J. M. Bioucas-Dias and M. A. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16, 2992-3004 (2007).

[CrossRef]

M. A. Figueiredo, J. M. Bioucas-Dias, and R. D. Nowak, “Majorization-minimization algorithms for wavelet-based image restoration,” IEEE Trans. Image Process. 16, 2980-2991 (2007).

[CrossRef]

M. A. Figueiredo and R. D. Nowak, “An EM algorithm for wavelet-based image restoration,” IEEE Trans. Image Process. 12, 906-916 (2003).

[CrossRef]

M. A. Figueiredo and R. D. Nowak, “Wavelet-based image estimation: an empirical Bayes approach using Jeffreys' noninformative prior,” IEEE Trans. Image Process. 10, 1322-1331 (2001).

[CrossRef]

J. M. Bioucas-Dias, M. A. Figueiredo, and J. P. Oliveira, “Total variation-based image deconvolution: a majorization-minimization approach,” in *2006 IEEE International Conference on Acoustics, Speech and Signal Processing* (IEEE, 2006), pp. 861-864.

J. E. Fowler, “The redundant discrete wavelet transform and additive noise,” IEEE Signal Process. Lett. 12, 629-632 (2005).

[CrossRef]

S. P. Ghael, A. M. Sayeed, and R. G. Baraniuk, “Improved wavelet denoising via empirical Wiener filtering,” Proc. SPIE 3169, 389-399 (1997).

[CrossRef]

R. C. Puetter, T. R. Gosnell, and A. Yahil, “Digital image reconstruction: deblurring and denoising,” Annu. Rev. Astron. Astrophys. 43, 139-194 (2005).

[CrossRef]

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10-12 (1996).

[CrossRef]

D. K. Hammond and E. P. Simoncelli, “Image modeling and denoising with orientation-adapted Gaussian scale mixtures,” IEEE Trans. Image Process. 17, 2089-2101 (2008).

[CrossRef]

J. Fessler and A. Hero, “Penalized maximum-likelihood image reconstruction using space-alternating generalized EM algorithms,” IEEE Trans. Image Process. 4, 1417-1429 (1995).

[CrossRef]

A. D. Hillery and R. T. Chin, “Iterative Wiener filters for image restoration,” IEEE Trans. Signal Process. 39, 1892-1899(1991).

[CrossRef]

T. Leonard and J. S. J. Hsu, *Bayesian Methods: an Analysis for Statisticians and Interdisciplinary Researchers* (China Machine Press, 2006).

H. Andrews and B. Hunt, *Digital Image Restoration* (Prentice-Hall, 1977).

A. Jalobeanu, L. Blanc-Féraud, and J. Zerubia, “An adaptive Gaussian model for satellite image deblurring,” IEEE Trans. Image Process. 13, 613-621 (2004).

[CrossRef]

A. Jalobeanu, L. Blanc-Féraud, and J. Zerubia, “Satellite image deconvolution using complex wavelet packets,” INRIA Research Report 3955 (Institut National de Recherche en Informatique et en Automatique, 2000), pp. 7-73.

R. Liu and J. Jia, “Reducing boundary artifacts in image deconvolution,” in Proceedings of 15th IEEE International Conference on Image Processing (IEEE, 2008), pp. 505-508.

I. M. Johnstone and B. W. Silverman, “Wavelet threshold estimators for data with correlated noise,” J. R. Statist. Soc. Ser. B 59, 319-351 (1997).

J. Kalifa, S. Mallat, and B. Rouge, “Deconvolution by thresholding in mirror wavelet bases,” IEEE Trans. Image Process. 12, 446-457 (2003).

[CrossRef]

J. Kalifa and S. Mallat, “Thresholding estimators for linear inverse problems and deconvolutions,” Ann. Statist. 31, 58-109 (2003).

J. Kalifa, S. Mallat, and B. Rouge, “Minimax solution of inverse problems and deconvolution by mirror wavelet thresholding,” Proc. SPIE 3813, 42-57 (1999).

[CrossRef]

J. Kalifa, S. Mallat, and B. Rouge, “Image deconvolution in mirror wavelet bases,” in *Proceedings of the 1998 International Conference on Image Processing* (IEEE, 1998), pp. 565-569.

D. Krishnan, P. Lin, and A. M. Yip, “A primal-dual active-set method for nonnegativity constrained total variation deblurring problems,” IEEE Trans. Image Process. 16, 2766-2777(2007).

[CrossRef]

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10-12 (1996).

[CrossRef]

T. Leonard and J. S. J. Hsu, *Bayesian Methods: an Analysis for Statisticians and Interdisciplinary Researchers* (China Machine Press, 2006).

D. Krishnan, P. Lin, and A. M. Yip, “A primal-dual active-set method for nonnegativity constrained total variation deblurring problems,” IEEE Trans. Image Process. 16, 2766-2777(2007).

[CrossRef]

P. Moulin and J. Liu, “Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors,” IEEE Trans. Inf. Theory 45, 909-919 (1999).

[CrossRef]

R. Liu and J. Jia, “Reducing boundary artifacts in image deconvolution,” in Proceedings of 15th IEEE International Conference on Image Processing (IEEE, 2008), pp. 505-508.

J. Kalifa and S. Mallat, “Thresholding estimators for linear inverse problems and deconvolutions,” Ann. Statist. 31, 58-109 (2003).

J. Kalifa, S. Mallat, and B. Rouge, “Deconvolution by thresholding in mirror wavelet bases,” IEEE Trans. Image Process. 12, 446-457 (2003).

[CrossRef]

J. Kalifa, S. Mallat, and B. Rouge, “Minimax solution of inverse problems and deconvolution by mirror wavelet thresholding,” Proc. SPIE 3813, 42-57 (1999).

[CrossRef]

J. Kalifa, S. Mallat, and B. Rouge, “Image deconvolution in mirror wavelet bases,” in *Proceedings of the 1998 International Conference on Image Processing* (IEEE, 1998), pp. 565-569.

S. Mallat, *A Wavelet Tour of Signal Processing* (Academic, 1998).

S. K. Patra, N. Mishra, R. Chandrakanth, and R. Ramachandran, “Image quality improvement through MTF compensation: a treatment to high resolution data,” Indian Cartogr. 22, 86-93 (2002).

P. Moulin and J. Liu, “Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors,” IEEE Trans. Inf. Theory 45, 909-919 (1999).

[CrossRef]

G. P. Nason and B. W. Silverman, “The stationary wavelet transform and some statistical applications,” Ph.D. dissertation (University of Bristol, 1995), pp. 1-19.

R. Neelamani, H. Choi, and R. Baraniuk, “ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems,” IEEE Trans. Signal Process. 52, 418-433 (2004).

[CrossRef]

M. A. Figueiredo, J. M. Bioucas-Dias, and R. D. Nowak, “Majorization-minimization algorithms for wavelet-based image restoration,” IEEE Trans. Image Process. 16, 2980-2991 (2007).

[CrossRef]

M. A. Figueiredo and R. D. Nowak, “An EM algorithm for wavelet-based image restoration,” IEEE Trans. Image Process. 12, 906-916 (2003).

[CrossRef]

M. A. Figueiredo and R. D. Nowak, “Wavelet-based image estimation: an empirical Bayes approach using Jeffreys' noninformative prior,” IEEE Trans. Image Process. 10, 1322-1331 (2001).

[CrossRef]

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10-12 (1996).

[CrossRef]

J. M. Bioucas-Dias, M. A. Figueiredo, and J. P. Oliveira, “Total variation-based image deconvolution: a majorization-minimization approach,” in *2006 IEEE International Conference on Acoustics, Speech and Signal Processing* (IEEE, 2006), pp. 861-864.

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent developments in total variation image restoration,” in *Mathematical Models of Computer Vision* (Springer-Verlag, 2005).

S. K. Patra, N. Mishra, R. Chandrakanth, and R. Ramachandran, “Image quality improvement through MTF compensation: a treatment to high resolution data,” Indian Cartogr. 22, 86-93 (2002).

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process. 12, 1338-1351(2003).

[CrossRef]

R. C. Puetter, T. R. Gosnell, and A. Yahil, “Digital image reconstruction: deblurring and denoising,” Annu. Rev. Astron. Astrophys. 43, 139-194 (2005).

[CrossRef]

S. K. Patra, N. Mishra, R. Chandrakanth, and R. Ramachandran, “Image quality improvement through MTF compensation: a treatment to high resolution data,” Indian Cartogr. 22, 86-93 (2002).

J. Kalifa, S. Mallat, and B. Rouge, “Deconvolution by thresholding in mirror wavelet bases,” IEEE Trans. Image Process. 12, 446-457 (2003).

[CrossRef]

J. Kalifa, S. Mallat, and B. Rouge, “Minimax solution of inverse problems and deconvolution by mirror wavelet thresholding,” Proc. SPIE 3813, 42-57 (1999).

[CrossRef]

J. Kalifa, S. Mallat, and B. Rouge, “Image deconvolution in mirror wavelet bases,” in *Proceedings of the 1998 International Conference on Image Processing* (IEEE, 1998), pp. 565-569.

S. P. Ghael, A. M. Sayeed, and R. G. Baraniuk, “Improved wavelet denoising via empirical Wiener filtering,” Proc. SPIE 3169, 389-399 (1997).

[CrossRef]

I. M. Johnstone and B. W. Silverman, “Wavelet threshold estimators for data with correlated noise,” J. R. Statist. Soc. Ser. B 59, 319-351 (1997).

G. P. Nason and B. W. Silverman, “The stationary wavelet transform and some statistical applications,” Ph.D. dissertation (University of Bristol, 1995), pp. 1-19.

D. K. Hammond and E. P. Simoncelli, “Image modeling and denoising with orientation-adapted Gaussian scale mixtures,” IEEE Trans. Image Process. 17, 2089-2101 (2008).

[CrossRef]

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process. 12, 1338-1351(2003).

[CrossRef]

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process. 12, 1338-1351(2003).

[CrossRef]

S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Process. 9, 1532-1546 (2000).

[CrossRef]

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process. 12, 1338-1351(2003).

[CrossRef]

F. Aghdasi and R. K. Ward, “Reduction of boundary artifacts in image restoration,” IEEE Trans. Image Process. 5, 611-618(1996).

[CrossRef]

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10-12 (1996).

[CrossRef]

R. C. Puetter, T. R. Gosnell, and A. Yahil, “Digital image reconstruction: deblurring and denoising,” Annu. Rev. Astron. Astrophys. 43, 139-194 (2005).

[CrossRef]

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent developments in total variation image restoration,” in *Mathematical Models of Computer Vision* (Springer-Verlag, 2005).

D. Krishnan, P. Lin, and A. M. Yip, “A primal-dual active-set method for nonnegativity constrained total variation deblurring problems,” IEEE Trans. Image Process. 16, 2766-2777(2007).

[CrossRef]

S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Process. 9, 1532-1546 (2000).

[CrossRef]

A. Jalobeanu, L. Blanc-Féraud, and J. Zerubia, “An adaptive Gaussian model for satellite image deblurring,” IEEE Trans. Image Process. 13, 613-621 (2004).

[CrossRef]

A. Jalobeanu, L. Blanc-Féraud, and J. Zerubia, “Satellite image deconvolution using complex wavelet packets,” INRIA Research Report 3955 (Institut National de Recherche en Informatique et en Automatique, 2000), pp. 7-73.

J. Kalifa and S. Mallat, “Thresholding estimators for linear inverse problems and deconvolutions,” Ann. Statist. 31, 58-109 (2003).

R. C. Puetter, T. R. Gosnell, and A. Yahil, “Digital image reconstruction: deblurring and denoising,” Annu. Rev. Astron. Astrophys. 43, 139-194 (2005).

[CrossRef]

D. L. Donoho, “Nonlinear solution of linear inverse problems by wavelet-vaguelette decomposition,” Appl. Comput. Harmon. Anal. 2, 101-126 (1995).

[CrossRef]

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10-12 (1996).

[CrossRef]

J. E. Fowler, “The redundant discrete wavelet transform and additive noise,” IEEE Signal Process. Lett. 12, 629-632 (2005).

[CrossRef]

F. Aghdasi and R. K. Ward, “Reduction of boundary artifacts in image restoration,” IEEE Trans. Image Process. 5, 611-618(1996).

[CrossRef]

J. M. Bioucas-Dias, “Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors,” IEEE Trans. Image Process. 15, 937-951 (2006).

[CrossRef]

A. Jalobeanu, L. Blanc-Féraud, and J. Zerubia, “An adaptive Gaussian model for satellite image deblurring,” IEEE Trans. Image Process. 13, 613-621 (2004).

[CrossRef]

J. M. Bioucas-Dias and M. A. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16, 2992-3004 (2007).

[CrossRef]

M. A. Figueiredo, J. M. Bioucas-Dias, and R. D. Nowak, “Majorization-minimization algorithms for wavelet-based image restoration,” IEEE Trans. Image Process. 16, 2980-2991 (2007).

[CrossRef]

M. A. Figueiredo and R. D. Nowak, “Wavelet-based image estimation: an empirical Bayes approach using Jeffreys' noninformative prior,” IEEE Trans. Image Process. 10, 1322-1331 (2001).

[CrossRef]

D. K. Hammond and E. P. Simoncelli, “Image modeling and denoising with orientation-adapted Gaussian scale mixtures,” IEEE Trans. Image Process. 17, 2089-2101 (2008).

[CrossRef]

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process. 12, 1338-1351(2003).

[CrossRef]

S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Process. 9, 1532-1546 (2000).

[CrossRef]

J. Kalifa, S. Mallat, and B. Rouge, “Deconvolution by thresholding in mirror wavelet bases,” IEEE Trans. Image Process. 12, 446-457 (2003).

[CrossRef]

D. Krishnan, P. Lin, and A. M. Yip, “A primal-dual active-set method for nonnegativity constrained total variation deblurring problems,” IEEE Trans. Image Process. 16, 2766-2777(2007).

[CrossRef]

M. A. Figueiredo and R. D. Nowak, “An EM algorithm for wavelet-based image restoration,” IEEE Trans. Image Process. 12, 906-916 (2003).

[CrossRef]

J. Fessler and A. Hero, “Penalized maximum-likelihood image reconstruction using space-alternating generalized EM algorithms,” IEEE Trans. Image Process. 4, 1417-1429 (1995).

[CrossRef]

P. Moulin and J. Liu, “Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors,” IEEE Trans. Inf. Theory 45, 909-919 (1999).

[CrossRef]

A. D. Hillery and R. T. Chin, “Iterative Wiener filters for image restoration,” IEEE Trans. Signal Process. 39, 1892-1899(1991).

[CrossRef]

R. Neelamani, H. Choi, and R. Baraniuk, “ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems,” IEEE Trans. Signal Process. 52, 418-433 (2004).

[CrossRef]

S. K. Patra, N. Mishra, R. Chandrakanth, and R. Ramachandran, “Image quality improvement through MTF compensation: a treatment to high resolution data,” Indian Cartogr. 22, 86-93 (2002).

I. M. Johnstone and B. W. Silverman, “Wavelet threshold estimators for data with correlated noise,” J. R. Statist. Soc. Ser. B 59, 319-351 (1997).

S. P. Ghael, A. M. Sayeed, and R. G. Baraniuk, “Improved wavelet denoising via empirical Wiener filtering,” Proc. SPIE 3169, 389-399 (1997).

[CrossRef]

J. Kalifa, S. Mallat, and B. Rouge, “Minimax solution of inverse problems and deconvolution by mirror wavelet thresholding,” Proc. SPIE 3813, 42-57 (1999).

[CrossRef]

A. Jalobeanu, L. Blanc-Féraud, and J. Zerubia, “Satellite image deconvolution using complex wavelet packets,” INRIA Research Report 3955 (Institut National de Recherche en Informatique et en Automatique, 2000), pp. 7-73.

J. M. Bioucas-Dias, M. A. Figueiredo, and J. P. Oliveira, “Total variation-based image deconvolution: a majorization-minimization approach,” in *2006 IEEE International Conference on Acoustics, Speech and Signal Processing* (IEEE, 2006), pp. 861-864.

H. Andrews and B. Hunt, *Digital Image Restoration* (Prentice-Hall, 1977).

T. Choi, “IKONOS satellite on orbit modulation transfer function (MTF) measurement using edge and pulse method,” Ph.D. dissertation (South Dakota State University, 2002), pp. 41-89.

M. Y. Zhou, *Deconvolution and Signal Recovery* (National Defence Industry Press, 2004).

J. Kalifa, S. Mallat, and B. Rouge, “Image deconvolution in mirror wavelet bases,” in *Proceedings of the 1998 International Conference on Image Processing* (IEEE, 1998), pp. 565-569.

S. Mallat, *A Wavelet Tour of Signal Processing* (Academic, 1998).

R. Liu and J. Jia, “Reducing boundary artifacts in image deconvolution,” in Proceedings of 15th IEEE International Conference on Image Processing (IEEE, 2008), pp. 505-508.

G. P. Nason and B. W. Silverman, “The stationary wavelet transform and some statistical applications,” Ph.D. dissertation (University of Bristol, 1995), pp. 1-19.

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent developments in total variation image restoration,” in *Mathematical Models of Computer Vision* (Springer-Verlag, 2005).

T. Leonard and J. S. J. Hsu, *Bayesian Methods: an Analysis for Statisticians and Interdisciplinary Researchers* (China Machine Press, 2006).