M. A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Wavelet denoising of multiframe optical coherence tomography data,” Biomed. Opt. Express 3(3), 572–589 (2012).

[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]

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]

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. 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. Express 2(10), 2871–2887 (2011).

[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]

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. Express 2(9), 2690–2697 (2011).

[CrossRef]
[PubMed]

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

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]

A. Wong, A. Mishra, K. Bizheva, and D. A. Clausi, “General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery,” Opt. Express 18(8), 8338–8352 (2010).

[CrossRef]
[PubMed]

Z. Jian, L. Yu, B. Rao, B. J. Tromberg, and Z. Chen, “Three-dimensional speckle suppression in Optical Coherence Tomography based on the curvelet transform,” Opt. Express 18(2), 1024–1032 (2010).

[CrossRef]
[PubMed]

N. Mohan, I. Stojanovic, W. C. Karl, B. E. A. Saleh, and M. C. Teich, “Compressed sensing in optical coherence tomography,” Proc. SPIE 7570, 75700L, 75700L-5 (2010).

[CrossRef]

X. Liu and J. U. Kang, “Compressive SD-OCT: the application of compressed sensing in spectral domain optical coherence tomography,” Opt. Express 18(21), 22010–22019 (2010).

[CrossRef]
[PubMed]

E. Lebed, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Rapid volumetric OCT image acquisition using compressive sampling,” Opt. Express 18(20), 21003–21012 (2010).

[CrossRef]
[PubMed]

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]

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]

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. Express 18(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]

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

[CrossRef]
[PubMed]

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).

[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).

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

[CrossRef]

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

[CrossRef]

M. Gargesha, M. W. Jenkins, A. M. Rollins, and D. L. Wilson, “Denoising and 4D visualization of OCT images,” Opt. Express 16(16), 12313–12333 (2008).

[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. Simulation 7(1), 214–241 (2008).

[CrossRef]

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]

M. A. Neifeld and J. Ke, “Optical architectures for compressive imaging,” Appl. Opt. 46(22), 5293–5303 (2007).

[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]

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]

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. Theory 52(2), 489–509 (2006).

[CrossRef]

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006).

[CrossRef]

S. Farsiu, M. Elad, and P. Milanfar, “A practical approach to superresolution,” Proc. SPIE 6077, 607703, 607703-15 (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]

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]

S. Jiao, R. Knighton, X. Huang, G. Gregori, and C. Puliafito, “Simultaneous acquisition of sectional and fundus ophthalmic images with spectral-domain optical coherence tomography,” Opt. Express 13(2), 444–452 (2005).

[CrossRef]
[PubMed]

B. Karamata, K. Hassler, M. Laubscher, and T. Lasser, “Speckle statistics in optical coherence tomography,” J. Opt. Soc. Am. A 22(4), 593–596 (2005).

[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]

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]

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]

G. Cincotti, G. Loi, and M. Pappalardo, “Frequency decomposition and compounding of ultrasound medical images with wavelet packets,” IEEE Trans. Med. Imaging 20(8), 764–771 (2001).

[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]

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]

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,” Science 254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

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]

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]

P. Bao and L. Zhang, “Noise reduction for magnetic resonance images via adaptive multiscale products thresholding,” IEEE Trans. Med. Imaging 22(9), 1089–1099 (2003).

[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. Express 2(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. Express 18(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. Theory 52(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,” Science 254(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. Express 18(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. Imaging 20(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, and G. Shi, “Centralized sparse representation for image restoration,” Proc. IEEE ICCV, 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]

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. Theory 52(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. Simulation 7(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. SPIE 6077, 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. Express 2(10), 2871–2887 (2011).

[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. Express 18(18), 19413–19428 (2010).

[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. 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]

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. SPIE 6077, 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,” Science 254(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. Express 2(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,” Science 254(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,” Science 254(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,” Science 254(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,” Science 254(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. Express 18(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. SPIE 7570, 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. Express 2(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. Express 18(20), 21003–21012 (2010).

[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. 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]

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,” Science 254(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. Imaging 20(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. Express 2(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. Express 18(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. Simulation 7(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. SPIE 6077, 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. SPIE 7570, 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. Imaging 20(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,” Science 254(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. Theory 52(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. SPIE 7570, 75700L, 75700L-5 (2010).

[CrossRef]

J. Mairal, G. Sapiro, and M. Elad, “Learning multiscale sparse representations for image and video restoration,” Multiscale Model. Simulation 7(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. Express 2(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. Express 18(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,” Science 254(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, 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. IEEE ICCV, 1259–1266 (2011).

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,” Science 254(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. SPIE 7570, 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,” Science 254(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. Theory 52(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. SPIE 7570, 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. Express 18(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. Express 2(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, 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. IEEE ICCV, 1259–1266 (2011).

P. Bao and L. Zhang, “Noise reduction for magnetic resonance images via adaptive multiscale products thresholding,” IEEE Trans. Med. Imaging 22(9), 1089–1099 (2003).

[CrossRef]
[PubMed]

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).

[CrossRef]
[PubMed]

M. A. Neifeld and J. Ke, “Optical architectures for compressive imaging,” Appl. Opt. 46(22), 5293–5303 (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]

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. Express 2(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. Express 2(9), 2690–2697 (2011).

[CrossRef]
[PubMed]

M. A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Wavelet denoising of multiframe optical coherence tomography data,” Biomed. Opt. Express 3(3), 572–589 (2012).

[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]

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]

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).

[CrossRef]
[PubMed]

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]

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]

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]

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]

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. Theory 52(2), 489–509 (2006).

[CrossRef]

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52(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. Imaging 20(8), 764–771 (2001).

[CrossRef]
[PubMed]

P. Bao and L. Zhang, “Noise reduction for magnetic resonance images via adaptive multiscale products thresholding,” IEEE Trans. Med. Imaging 22(9), 1089–1099 (2003).

[CrossRef]
[PubMed]

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]

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]

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. Simulation 7(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]

Z. Jian, L. Yu, B. Rao, B. J. Tromberg, and Z. Chen, “Three-dimensional speckle suppression in Optical Coherence Tomography based on the curvelet transform,” Opt. Express 18(2), 1024–1032 (2010).

[CrossRef]
[PubMed]

X. Liu and J. U. Kang, “Compressive SD-OCT: the application of compressed sensing in spectral domain optical coherence tomography,” Opt. Express 18(21), 22010–22019 (2010).

[CrossRef]
[PubMed]

E. Lebed, P. J. Mackenzie, M. V. Sarunic, and M. F. Beg, “Rapid volumetric OCT image acquisition using compressive sampling,” Opt. Express 18(20), 21003–21012 (2010).

[CrossRef]
[PubMed]

S. Jiao, R. Knighton, X. Huang, G. Gregori, and C. Puliafito, “Simultaneous acquisition of sectional and fundus ophthalmic images with spectral-domain optical coherence tomography,” Opt. Express 13(2), 444–452 (2005).

[CrossRef]
[PubMed]

A. Wong, A. Mishra, K. Bizheva, and D. A. Clausi, “General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery,” Opt. Express 18(8), 8338–8352 (2010).

[CrossRef]
[PubMed]

M. Gargesha, M. W. Jenkins, A. M. Rollins, and D. L. Wilson, “Denoising and 4D visualization of OCT images,” Opt. Express 16(16), 12313–12333 (2008).

[CrossRef]
[PubMed]

M. Choma, M. Sarunic, C. Yang, and J. Izatt, “Sensitivity advantage of swept source and Fourier domain optical coherence tomography,” Opt. Express 11(18), 2183–2189 (2003).

[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. Express 18(18), 19413–19428 (2010).

[CrossRef]
[PubMed]

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

S. Farsiu, M. Elad, and P. Milanfar, “A practical approach to superresolution,” Proc. SPIE 6077, 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. SPIE 7570, 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,” Science 254(5035), 1178–1181 (1991).

[CrossRef]
[PubMed]

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. D. Robinson, S. J. Chiu, C. A. Toth, J. Izatt, J. Y. Lo, and S. Farsiu, “Novel applications of super-resolution in medical imaging,” in Super-Resolution Imaging, P. Milanfar, ed. (CRC Press, 2010), pp. 383–412.

W. Dong, X. Li, L. Zhang, and G. Shi, “Sparsity-based image denoising via dictionary learning and structural clustering,” in 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2011), pp. 457–464.

“Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study (A2ASDOCT)” (2008–2013), http://clinicaltrials.gov/ct2/show/NCT00734487

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Adaptive Wiener denoising using a Gaussian scale mixture model in the wavelet domain,” in 2001 International Conference on Image Processing, 2001. Proceedings (2001), Vol. 2, pp. 37–40.

H. Takeda, S. Farsiu, and P. Milanfar, “Robust kernel regression for restoration and reconstruction of images from sparse noisy data,” in 2006 IEEE International Conference on Image Processing (2006), pp. 1257–1260.

W. Dong, “Sparsity-based Image Denoising via Dictionary Learning and Structural Clustering,” http://www4.comp.polyu.edu.hk/~cslzhang/code.htm .

A. Foi, “Noise estimation and removal in MR imaging: The variance stabilization approach,” in 2011 IEEE International Symposium on Biomedical Imaging: from Nano to Macro (2011), pp. 1809–1814.

Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, “Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition,” in 1993 Conference Record of The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers (1993), Vol. 1, pp. 40–44.