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B. Ophir, M. Lustig, and M. Elad, “Multi-scale dictionary learning using wavelets,” IEEE J. Sel. Top. Signal Process.5(5), 1014–1024 (2011).

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W. Dong, L. Zhang, and G. Shi, “Centralized sparse representation for image restoration,” Proc. IEEEICCV, 1259–1266 (2011).

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R. Rubinstein, M. Zibulevsky, and M. Elad, “Double sparsity: learning sparse dictionaries for sparse signal approximation,” IEEE Trans. Signal Process.58(3), 1553–1564 (2010).

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M. D. Robinson, C. A. Toth, J. Y. Lo, and S. Farsiu, “Efﬁcient fourier-wavelet super-resolution,” IEEE Trans. Image Process.19(10), 2669–2681 (2010).

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S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express18(18), 19413–19428 (2010).

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G. T. Chong, S. Farsiu, S. F. Freedman, N. Sarin, A. F. Koreishi, J. A. Izatt, and C. A. Toth, “Abnormal foveal morphology in ocular albinism imaged with spectral-domain optical coherence tomography,” Arch. Ophthalmol.127(1), 37–44 (2009).

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P. Chatterjee and P. Milanfar, “Clustering-based denoising with locally learned dictionaries,” IEEE Trans. Image Process.18(7), 1438–1451 (2009).

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A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Rev.51(1), 34–81 (2009).

[CrossRef]

A. W. Scott, S. Farsiu, L. B. Enyedi, D. K. Wallace, and C. A. Toth, “Imaging the infant retina with a hand-held spectral-domain optical coherence tomography device,” Am. J. Ophthalmol.147(2), 364–373.e2 (2009).

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S. Farsiu, J. Christofferson, B. Eriksson, P. Milanfar, B. Friedlander, A. Shakouri, and R. Nowak, “Statistical detection and imaging of objects hidden in turbid media using ballistic photons,” Appl. Opt.46(23), 5805–5822 (2007).

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[CrossRef]

D. C. Adler, T. H. Ko, and J. G. Fujimoto, “Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter,” Opt. Lett.29(24), 2878–2880 (2004).

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M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process.15(12), 3736–3745 (2006).

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M. Aharon, M. Elad, and A. M. Bruckstein, “The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process.54(11), 4311–4322 (2006).

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M. Young, E. Lebed, Y. Jian, P. J. Mackenzie, M. F. Beg, and M. V. Sarunic, “Real-time high-speed volumetric imaging using compressive sampling optical coherence tomography,” Biomed. Opt. Express2(9), 2690–2697 (2011).

[CrossRef]
[PubMed]

E. Lebed, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Rapid volumetric OCT image acquisition using compressive sampling,” Opt. Express18(20), 21003–21012 (2010).

[CrossRef]
[PubMed]

F. Luisier, T. Blu, and M. Unser, “A new SURE approach to image denoising: interscale orthonormal wavelet thresholding,” IEEE Trans. Image Process.16(3), 593–606 (2007).

[CrossRef]
[PubMed]

D. Healy and D. J. Brady, “Compression at the physical interface,” IEEE Signal Process. Mag.25(2), 67–71 (2008).

[CrossRef]

A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Rev.51(1), 34–81 (2009).

[CrossRef]

M. Aharon, M. Elad, and A. M. Bruckstein, “The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process.54(11), 4311–4322 (2006).

[CrossRef]

A. Buades, B. Coll, and J.-M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model. Simul.4(2), 490–530 (2005).

[CrossRef]

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory52(2), 489–509 (2006).

[CrossRef]

S. Ji, Y. Xue, and L. Carin, “Bayesian compressive sensing,” IEEE Trans. Signal Process.56(6), 2346–2356 (2008).

[CrossRef]

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

[CrossRef]
[PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

P. Chatterjee and P. Milanfar, “Practical bounds on image denoising: from estimation to information,” IEEE Trans. Image Process.20(5), 1221–1233 (2011).

[CrossRef]
[PubMed]

P. Chatterjee and P. Milanfar, “Clustering-based denoising with locally learned dictionaries,” IEEE Trans. Image Process.18(7), 1438–1451 (2009).

[CrossRef]
[PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci.53(1), 53–61 (2012).

[CrossRef]
[PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express18(18), 19413–19428 (2010).

[CrossRef]
[PubMed]

G. T. Chong, S. Farsiu, S. F. Freedman, N. Sarin, A. F. Koreishi, J. A. Izatt, and C. A. Toth, “Abnormal foveal morphology in ocular albinism imaged with spectral-domain optical coherence tomography,” Arch. Ophthalmol.127(1), 37–44 (2009).

[CrossRef]
[PubMed]

G. Cincotti, G. Loi, and M. Pappalardo, “Frequency decomposition and compounding of ultrasound medical images with wavelet packets,” IEEE Trans. Med. Imaging20(8), 764–771 (2001).

[CrossRef]
[PubMed]

A. Buades, B. Coll, and J.-M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model. Simul.4(2), 490–530 (2005).

[CrossRef]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process.16(8), 2080–2095 (2007).

[CrossRef]
[PubMed]

I. Daubechies, R. DeVore, M. Fornasier, and C. S. Gunturk, “Iteratively reweighted least squares minimization for sparse recovery,” Commun. Pure Appl. Math.63(1), 1–38 (2010).

[CrossRef]

I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math.57(11), 1413–1457 (2004).

[CrossRef]

I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math.57(11), 1413–1457 (2004).

[CrossRef]

I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math.57(11), 1413–1457 (2004).

[CrossRef]

I. Daubechies, R. DeVore, M. Fornasier, and C. S. Gunturk, “Iteratively reweighted least squares minimization for sparse recovery,” Commun. Pure Appl. Math.63(1), 1–38 (2010).

[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(7), 1838–1857 (2011).

[CrossRef]
[PubMed]

W. Dong, L. Zhang, and G. Shi, “Centralized sparse representation for image restoration,” Proc. IEEEICCV, 1259–1266 (2011).

A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Rev.51(1), 34–81 (2009).

[CrossRef]

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory52(4), 1289–1306 (2006).

[CrossRef]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process.16(8), 2080–2095 (2007).

[CrossRef]
[PubMed]

B. Ophir, M. Lustig, and M. Elad, “Multi-scale dictionary learning using wavelets,” IEEE J. Sel. Top. Signal Process.5(5), 1014–1024 (2011).

[CrossRef]

R. Rubinstein, M. Zibulevsky, and M. Elad, “Double sparsity: learning sparse dictionaries for sparse signal approximation,” IEEE Trans. Signal Process.58(3), 1553–1564 (2010).

[CrossRef]

A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Rev.51(1), 34–81 (2009).

[CrossRef]

J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. Image Process.17(1), 53–69 (2008).

[CrossRef]
[PubMed]

J. Mairal, G. Sapiro, and M. Elad, “Learning multiscale sparse representations for image and video restoration,” Multiscale Model. Simulation7(1), 214–241 (2008).

[CrossRef]

M. Aharon, M. Elad, and A. M. Bruckstein, “The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process.54(11), 4311–4322 (2006).

[CrossRef]

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process.15(12), 3736–3745 (2006).

[CrossRef]
[PubMed]

S. Farsiu, M. Elad, and P. Milanfar, “A practical approach to superresolution,” Proc. SPIE6077, 607703, 607703-15 (2006).

[CrossRef]

A. W. Scott, S. Farsiu, L. B. Enyedi, D. K. Wallace, and C. A. Toth, “Imaging the infant retina with a hand-held spectral-domain optical coherence tomography device,” Am. J. Ophthalmol.147(2), 364–373.e2 (2009).

[CrossRef]
[PubMed]

S. Li, L. Fang, and H. Yin, “An efficient dictionary learning algorithm and its application to 3-D medical image denoising,” IEEE Trans. Biomed. Eng.59(2), 417–427 (2012).

[CrossRef]
[PubMed]

S. Li and L. Fang, “Signal denoising with random refined orthogonal matching pursuit,” IEEE Trans. Instrum. Meas.61(1), 26–34 (2012).

[CrossRef]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci.53(1), 53–61 (2012).

[CrossRef]
[PubMed]

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomed. Opt. Express2(10), 2871–2887 (2011).

[CrossRef]
[PubMed]

M. D. Robinson, C. A. Toth, J. Y. Lo, and S. Farsiu, “Efﬁcient fourier-wavelet super-resolution,” IEEE Trans. Image Process.19(10), 2669–2681 (2010).

[CrossRef]
[PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express18(18), 19413–19428 (2010).

[CrossRef]
[PubMed]

G. T. Chong, S. Farsiu, S. F. Freedman, N. Sarin, A. F. Koreishi, J. A. Izatt, and C. A. Toth, “Abnormal foveal morphology in ocular albinism imaged with spectral-domain optical coherence tomography,” Arch. Ophthalmol.127(1), 37–44 (2009).

[CrossRef]
[PubMed]

A. W. Scott, S. Farsiu, L. B. Enyedi, D. K. Wallace, and C. A. Toth, “Imaging the infant retina with a hand-held spectral-domain optical coherence tomography device,” Am. J. Ophthalmol.147(2), 364–373.e2 (2009).

[CrossRef]
[PubMed]

S. Farsiu, J. Christofferson, B. Eriksson, P. Milanfar, B. Friedlander, A. Shakouri, and R. Nowak, “Statistical detection and imaging of objects hidden in turbid media using ballistic photons,” Appl. Opt.46(23), 5805–5822 (2007).

[CrossRef]
[PubMed]

H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process.16(2), 349–366 (2007).

[CrossRef]
[PubMed]

S. Farsiu, M. Elad, and P. Milanfar, “A practical approach to superresolution,” Proc. SPIE6077, 607703, 607703-15 (2006).

[CrossRef]

S. J. Wright, R. D. Nowak, and M. A. T. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE J. Sel. Top. Signal Process.57, 2479–2493 (2009).

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process.16(8), 2080–2095 (2007).

[CrossRef]
[PubMed]

I. Daubechies, R. DeVore, M. Fornasier, and C. S. Gunturk, “Iteratively reweighted least squares minimization for sparse recovery,” Commun. Pure Appl. Math.63(1), 1–38 (2010).

[CrossRef]

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomed. Opt. Express2(10), 2871–2887 (2011).

[CrossRef]
[PubMed]

G. T. Chong, S. Farsiu, S. F. Freedman, N. Sarin, A. F. Koreishi, J. A. Izatt, and C. A. Toth, “Abnormal foveal morphology in ocular albinism imaged with spectral-domain optical coherence tomography,” Arch. Ophthalmol.127(1), 37–44 (2009).

[CrossRef]
[PubMed]

D. C. Adler, T. H. Ko, and J. G. Fujimoto, “Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter,” Opt. Lett.29(24), 2878–2880 (2004).

[CrossRef]
[PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

I. Daubechies, R. DeVore, M. Fornasier, and C. S. Gunturk, “Iteratively reweighted least squares minimization for sparse recovery,” Commun. Pure Appl. Math.63(1), 1–38 (2010).

[CrossRef]

D. Healy and D. J. Brady, “Compression at the physical interface,” IEEE Signal Process. Mag.25(2), 67–71 (2008).

[CrossRef]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci.53(1), 53–61 (2012).

[CrossRef]
[PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express18(18), 19413–19428 (2010).

[CrossRef]
[PubMed]

G. T. Chong, S. Farsiu, S. F. Freedman, N. Sarin, A. F. Koreishi, J. A. Izatt, and C. A. Toth, “Abnormal foveal morphology in ocular albinism imaged with spectral-domain optical coherence tomography,” Arch. Ophthalmol.127(1), 37–44 (2009).

[CrossRef]
[PubMed]

S. Ji, Y. Xue, and L. Carin, “Bayesian compressive sensing,” IEEE Trans. Signal Process.56(6), 2346–2356 (2008).

[CrossRef]

N. Mohan, I. Stojanovic, W. C. Karl, B. E. A. Saleh, and M. C. Teich, “Compressed sensing in optical coherence tomography,” Proc. SPIE7570, 75700L, 75700L-5 (2010).

[CrossRef]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process.16(8), 2080–2095 (2007).

[CrossRef]
[PubMed]

G. T. Chong, S. Farsiu, S. F. Freedman, N. Sarin, A. F. Koreishi, J. A. Izatt, and C. A. Toth, “Abnormal foveal morphology in ocular albinism imaged with spectral-domain optical coherence tomography,” Arch. Ophthalmol.127(1), 37–44 (2009).

[CrossRef]
[PubMed]

M. Young, E. Lebed, Y. Jian, P. J. Mackenzie, M. F. Beg, and M. V. Sarunic, “Real-time high-speed volumetric imaging using compressive sampling optical coherence tomography,” Biomed. Opt. Express2(9), 2690–2697 (2011).

[CrossRef]
[PubMed]

E. Lebed, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Rapid volumetric OCT image acquisition using compressive sampling,” Opt. Express18(20), 21003–21012 (2010).

[CrossRef]
[PubMed]

S. Li, L. Fang, and H. Yin, “An efficient dictionary learning algorithm and its application to 3-D medical image denoising,” IEEE Trans. Biomed. Eng.59(2), 417–427 (2012).

[CrossRef]
[PubMed]

S. Li and L. Fang, “Signal denoising with random refined orthogonal matching pursuit,” IEEE Trans. Instrum. Meas.61(1), 26–34 (2012).

[CrossRef]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

M. D. Robinson, C. A. Toth, J. Y. Lo, and S. Farsiu, “Efﬁcient fourier-wavelet super-resolution,” IEEE Trans. Image Process.19(10), 2669–2681 (2010).

[CrossRef]
[PubMed]

G. Cincotti, G. Loi, and M. Pappalardo, “Frequency decomposition and compounding of ultrasound medical images with wavelet packets,” IEEE Trans. Med. Imaging20(8), 764–771 (2001).

[CrossRef]
[PubMed]

F. Luisier, T. Blu, and M. Unser, “A new SURE approach to image denoising: interscale orthonormal wavelet thresholding,” IEEE Trans. Image Process.16(3), 593–606 (2007).

[CrossRef]
[PubMed]

B. Ophir, M. Lustig, and M. Elad, “Multi-scale dictionary learning using wavelets,” IEEE J. Sel. Top. Signal Process.5(5), 1014–1024 (2011).

[CrossRef]

M. Young, E. Lebed, Y. Jian, P. J. Mackenzie, M. F. Beg, and M. V. Sarunic, “Real-time high-speed volumetric imaging using compressive sampling optical coherence tomography,” Biomed. Opt. Express2(9), 2690–2697 (2011).

[CrossRef]
[PubMed]

E. Lebed, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Rapid volumetric OCT image acquisition using compressive sampling,” Opt. Express18(20), 21003–21012 (2010).

[CrossRef]
[PubMed]

J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. Image Process.17(1), 53–69 (2008).

[CrossRef]
[PubMed]

J. Mairal, G. Sapiro, and M. Elad, “Learning multiscale sparse representations for image and video restoration,” Multiscale Model. Simulation7(1), 214–241 (2008).

[CrossRef]

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng.50(7), 072601–072613 (2011).

[CrossRef]

P. Chatterjee and P. Milanfar, “Practical bounds on image denoising: from estimation to information,” IEEE Trans. Image Process.20(5), 1221–1233 (2011).

[CrossRef]
[PubMed]

P. Chatterjee and P. Milanfar, “Clustering-based denoising with locally learned dictionaries,” IEEE Trans. Image Process.18(7), 1438–1451 (2009).

[CrossRef]
[PubMed]

H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process.16(2), 349–366 (2007).

[CrossRef]
[PubMed]

S. Farsiu, J. Christofferson, B. Eriksson, P. Milanfar, B. Friedlander, A. Shakouri, and R. Nowak, “Statistical detection and imaging of objects hidden in turbid media using ballistic photons,” Appl. Opt.46(23), 5805–5822 (2007).

[CrossRef]
[PubMed]

S. Farsiu, M. Elad, and P. Milanfar, “A practical approach to superresolution,” Proc. SPIE6077, 607703, 607703-15 (2006).

[CrossRef]

N. Mohan, I. Stojanovic, W. C. Karl, B. E. A. Saleh, and M. C. Teich, “Compressed sensing in optical coherence tomography,” Proc. SPIE7570, 75700L, 75700L-5 (2010).

[CrossRef]

A. Buades, B. Coll, and J.-M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model. Simul.4(2), 490–530 (2005).

[CrossRef]

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng.50(7), 072601–072613 (2011).

[CrossRef]

S. J. Wright, R. D. Nowak, and M. A. T. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE J. Sel. Top. Signal Process.57, 2479–2493 (2009).

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci.53(1), 53–61 (2012).

[CrossRef]
[PubMed]

B. Ophir, M. Lustig, and M. Elad, “Multi-scale dictionary learning using wavelets,” IEEE J. Sel. Top. Signal Process.5(5), 1014–1024 (2011).

[CrossRef]

G. Cincotti, G. Loi, and M. Pappalardo, “Frequency decomposition and compounding of ultrasound medical images with wavelet packets,” IEEE Trans. Med. Imaging20(8), 764–771 (2001).

[CrossRef]
[PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

M. D. Robinson, C. A. Toth, J. Y. Lo, and S. Farsiu, “Efﬁcient fourier-wavelet super-resolution,” IEEE Trans. Image Process.19(10), 2669–2681 (2010).

[CrossRef]
[PubMed]

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory52(2), 489–509 (2006).

[CrossRef]

R. Rubinstein, M. Zibulevsky, and M. Elad, “Double sparsity: learning sparse dictionaries for sparse signal approximation,” IEEE Trans. Signal Process.58(3), 1553–1564 (2010).

[CrossRef]

P. Thévenaz, U. E. Ruttimann, and M. Unser, “A pyramid approach to subpixel registration based on intensity,” IEEE Trans. Image Process.7(1), 27–41 (1998).

[CrossRef]
[PubMed]

N. Mohan, I. Stojanovic, W. C. Karl, B. E. A. Saleh, and M. C. Teich, “Compressed sensing in optical coherence tomography,” Proc. SPIE7570, 75700L, 75700L-5 (2010).

[CrossRef]

J. Mairal, G. Sapiro, and M. Elad, “Learning multiscale sparse representations for image and video restoration,” Multiscale Model. Simulation7(1), 214–241 (2008).

[CrossRef]

J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. Image Process.17(1), 53–69 (2008).

[CrossRef]
[PubMed]

G. T. Chong, S. Farsiu, S. F. Freedman, N. Sarin, A. F. Koreishi, J. A. Izatt, and C. A. Toth, “Abnormal foveal morphology in ocular albinism imaged with spectral-domain optical coherence tomography,” Arch. Ophthalmol.127(1), 37–44 (2009).

[CrossRef]
[PubMed]

M. Young, E. Lebed, Y. Jian, P. J. Mackenzie, M. F. Beg, and M. V. Sarunic, “Real-time high-speed volumetric imaging using compressive sampling optical coherence tomography,” Biomed. Opt. Express2(9), 2690–2697 (2011).

[CrossRef]
[PubMed]

E. Lebed, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Rapid volumetric OCT image acquisition using compressive sampling,” Opt. Express18(20), 21003–21012 (2010).

[CrossRef]
[PubMed]

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt.4(1), 95–105 (1999).

[CrossRef]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

A. W. Scott, S. Farsiu, L. B. Enyedi, D. K. Wallace, and C. A. Toth, “Imaging the infant retina with a hand-held spectral-domain optical coherence tomography device,” Am. J. Ophthalmol.147(2), 364–373.e2 (2009).

[CrossRef]
[PubMed]

W. Dong, L. Zhang, and G. Shi, “Centralized sparse representation for image restoration,” Proc. IEEEICCV, 1259–1266 (2011).

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(7), 1838–1857 (2011).

[CrossRef]
[PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

N. Mohan, I. Stojanovic, W. C. Karl, B. E. A. Saleh, and M. C. Teich, “Compressed sensing in optical coherence tomography,” Proc. SPIE7570, 75700L, 75700L-5 (2010).

[CrossRef]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process.16(2), 349–366 (2007).

[CrossRef]
[PubMed]

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory52(2), 489–509 (2006).

[CrossRef]

N. Mohan, I. Stojanovic, W. C. Karl, B. E. A. Saleh, and M. C. Teich, “Compressed sensing in optical coherence tomography,” Proc. SPIE7570, 75700L, 75700L-5 (2010).

[CrossRef]

P. Thévenaz, U. E. Ruttimann, and M. Unser, “A pyramid approach to subpixel registration based on intensity,” IEEE Trans. Image Process.7(1), 27–41 (1998).

[CrossRef]
[PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci.53(1), 53–61 (2012).

[CrossRef]
[PubMed]

M. D. Robinson, C. A. Toth, J. Y. Lo, and S. Farsiu, “Efﬁcient fourier-wavelet super-resolution,” IEEE Trans. Image Process.19(10), 2669–2681 (2010).

[CrossRef]
[PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express18(18), 19413–19428 (2010).

[CrossRef]
[PubMed]

G. T. Chong, S. Farsiu, S. F. Freedman, N. Sarin, A. F. Koreishi, J. A. Izatt, and C. A. Toth, “Abnormal foveal morphology in ocular albinism imaged with spectral-domain optical coherence tomography,” Arch. Ophthalmol.127(1), 37–44 (2009).

[CrossRef]
[PubMed]

A. W. Scott, S. Farsiu, L. B. Enyedi, D. K. Wallace, and C. A. Toth, “Imaging the infant retina with a hand-held spectral-domain optical coherence tomography device,” Am. J. Ophthalmol.147(2), 364–373.e2 (2009).

[CrossRef]
[PubMed]

F. Luisier, T. Blu, and M. Unser, “A new SURE approach to image denoising: interscale orthonormal wavelet thresholding,” IEEE Trans. Image Process.16(3), 593–606 (2007).

[CrossRef]
[PubMed]

P. Thévenaz, U. E. Ruttimann, and M. Unser, “A pyramid approach to subpixel registration based on intensity,” IEEE Trans. Image Process.7(1), 27–41 (1998).

[CrossRef]
[PubMed]

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

[CrossRef]
[PubMed]

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomed. Opt. Express2(10), 2871–2887 (2011).

[CrossRef]
[PubMed]

A. W. Scott, S. Farsiu, L. B. Enyedi, D. K. Wallace, and C. A. Toth, “Imaging the infant retina with a hand-held spectral-domain optical coherence tomography device,” Am. J. Ophthalmol.147(2), 364–373.e2 (2009).

[CrossRef]
[PubMed]

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng.50(7), 072601–072613 (2011).

[CrossRef]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci.53(1), 53–61 (2012).

[CrossRef]
[PubMed]

S. J. Wright, R. D. Nowak, and M. A. T. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE J. Sel. Top. Signal Process.57, 2479–2493 (2009).

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(7), 1838–1857 (2011).

[CrossRef]
[PubMed]

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt.4(1), 95–105 (1999).

[CrossRef]

S. Ji, Y. Xue, and L. Carin, “Bayesian compressive sensing,” IEEE Trans. Signal Process.56(6), 2346–2356 (2008).

[CrossRef]

S. Li, L. Fang, and H. Yin, “An efficient dictionary learning algorithm and its application to 3-D medical image denoising,” IEEE Trans. Biomed. Eng.59(2), 417–427 (2012).

[CrossRef]
[PubMed]

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

[CrossRef]
[PubMed]

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt.4(1), 95–105 (1999).

[CrossRef]

W. Dong, L. Zhang, and G. Shi, “Centralized sparse representation for image restoration,” Proc. IEEEICCV, 1259–1266 (2011).

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(7), 1838–1857 (2011).

[CrossRef]
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P. Bao and L. Zhang, “Noise reduction for magnetic resonance images via adaptive multiscale products thresholding,” IEEE Trans. Med. Imaging22(9), 1089–1099 (2003).

[CrossRef]
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R. Rubinstein, M. Zibulevsky, and M. Elad, “Double sparsity: learning sparse dictionaries for sparse signal approximation,” IEEE Trans. Signal Process.58(3), 1553–1564 (2010).

[CrossRef]

A. W. Scott, S. Farsiu, L. B. Enyedi, D. K. Wallace, and C. A. Toth, “Imaging the infant retina with a hand-held spectral-domain optical coherence tomography device,” Am. J. Ophthalmol.147(2), 364–373.e2 (2009).

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[CrossRef]
[PubMed]

G. T. Chong, S. Farsiu, S. F. Freedman, N. Sarin, A. F. Koreishi, J. A. Izatt, and C. A. Toth, “Abnormal foveal morphology in ocular albinism imaged with spectral-domain optical coherence tomography,” Arch. Ophthalmol.127(1), 37–44 (2009).

[CrossRef]
[PubMed]

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomed. Opt. Express2(10), 2871–2887 (2011).

[CrossRef]
[PubMed]

M. Young, E. Lebed, Y. Jian, P. J. Mackenzie, M. F. Beg, and M. V. Sarunic, “Real-time high-speed volumetric imaging using compressive sampling optical coherence tomography,” Biomed. Opt. Express2(9), 2690–2697 (2011).

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B. Ophir, M. Lustig, and M. Elad, “Multi-scale dictionary learning using wavelets,” IEEE J. Sel. Top. Signal Process.5(5), 1014–1024 (2011).

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S. J. Wright, R. D. Nowak, and M. A. T. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE J. Sel. Top. Signal Process.57, 2479–2493 (2009).

D. Healy and D. J. Brady, “Compression at the physical interface,” IEEE Signal Process. Mag.25(2), 67–71 (2008).

[CrossRef]

S. Li, L. Fang, and H. Yin, “An efficient dictionary learning algorithm and its application to 3-D medical image denoising,” IEEE Trans. Biomed. Eng.59(2), 417–427 (2012).

[CrossRef]
[PubMed]

J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. Image Process.17(1), 53–69 (2008).

[CrossRef]
[PubMed]

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

[CrossRef]
[PubMed]

P. Chatterjee and P. Milanfar, “Practical bounds on image denoising: from estimation to information,” IEEE Trans. Image Process.20(5), 1221–1233 (2011).

[CrossRef]
[PubMed]

P. Chatterjee and P. Milanfar, “Clustering-based denoising with locally learned dictionaries,” IEEE Trans. Image Process.18(7), 1438–1451 (2009).

[CrossRef]
[PubMed]

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(7), 1838–1857 (2011).

[CrossRef]
[PubMed]

H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process.16(2), 349–366 (2007).

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M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process.15(12), 3736–3745 (2006).

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K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process.16(8), 2080–2095 (2007).

[CrossRef]
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[CrossRef]
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[CrossRef]
[PubMed]

M. D. Robinson, C. A. Toth, J. Y. Lo, and S. Farsiu, “Efﬁcient fourier-wavelet super-resolution,” IEEE Trans. Image Process.19(10), 2669–2681 (2010).

[CrossRef]
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[CrossRef]

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory52(4), 1289–1306 (2006).

[CrossRef]

S. Li and L. Fang, “Signal denoising with random refined orthogonal matching pursuit,” IEEE Trans. Instrum. Meas.61(1), 26–34 (2012).

[CrossRef]

G. Cincotti, G. Loi, and M. Pappalardo, “Frequency decomposition and compounding of ultrasound medical images with wavelet packets,” IEEE Trans. Med. Imaging20(8), 764–771 (2001).

[CrossRef]
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[CrossRef]

S. Ji, Y. Xue, and L. Carin, “Bayesian compressive sensing,” IEEE Trans. Signal Process.56(6), 2346–2356 (2008).

[CrossRef]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci.53(1), 53–61 (2012).

[CrossRef]
[PubMed]

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt.4(1), 95–105 (1999).

[CrossRef]

A. Buades, B. Coll, and J.-M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model. Simul.4(2), 490–530 (2005).

[CrossRef]

J. Mairal, G. Sapiro, and M. Elad, “Learning multiscale sparse representations for image and video restoration,” Multiscale Model. Simulation7(1), 214–241 (2008).

[CrossRef]

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng.50(7), 072601–072613 (2011).

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