102
L. Fang and S. Li
24. S. Chen, H. Liu, Z. Hu, H. Zhang, P. Shi, Y. Chen, Simultaneous reconstruction and segmentation of dynamic pet via low-rank and sparse matrix decomposition. IEEE Trans. Biomed. Eng.
62, 1784–1795 (2015)
25. M. Aharon, M. Elad, A.M. Bruckstein, The K-SVD: an algorithm for designing of overcomplete
dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311–4322 (2006)
26. L. Fang, S. Li, X. Kang, J.A. Izatt, S. Farsiu, 3-D Adaptive sparsity based image compression
with applications to optical coherence tomography. IEEE Trans. Med. Imag. 34, 1306–1320
(2015)
27. R. Kafieh, H. Rabbani, I. Selesnick, Three dimensional data-driven multi scale atomic representation of optical coherence tomography. IEEE Trans. Med. Imag. 34, 1042–1062 (2015)
28. M. Elad, M. Aharon, Image denoising via sparse and redundant representations over learned
dictionaries. IEEE Trans. Image Process. 15, 3736–3745 (2006)
29. J. Yang, J. Wright, T.S. Huang, Y. Ma, Image super-resolution via sparse representation. IEEE
Trans. Image Process. 19, 2861–2873 (2010)
30. O. Bryt, M. Elad, Compression of facial images using the K-SVD algorithm. J. Vis. Commun.
Image Represent. 19, 270–282 (2008)
31. J. Zepeda, C. Guillemot, E. Kijak, Image compression using sparse representations and the
iteration-tuned and aligned dictionary. IEEE J. Sel. Topics Signal Process. 5, 1061–1073 (2011)
32. K. Skretting, K. Engan, Image compression using learned dictionaries by RLS-DLA and compared with K-SVD. in Proceedings of IEEE International Conference on Acoustics Speech
Signal Processing, pp. 1517–1520 (2011)
33. A. Foi, Noise estimation and removal in MR imaging. in Proceeding of IEEE International
Symposium Biomedical Imaging (2011), pp. 1809–1814
34. S.G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal
Process. 41, 3397–3415 (1993)
35. P. Chatterjee, P. Milanfar, Clustering-based denoising with locally learned dictionaries. IEEE
Trans. Image Process. 18, 1438–1451 (2009)
36. W. Dong, L. Zhang, G. Shi, X. Wu, Image deblurring and super-resolution by adaptive sparse
domain selection and adaptive regularization. IEEE Trans. Image Process. 20, 1838–1857
(2011)
37. W. Dong, L. Zhang, G. Shi, Centralized sparse representation for image restoration. in IEEE
International Conference on Computer Vision, pp. 1259–1266 (2011)
38. J. Mairal, F. Bach, J. Ponce, G. Sapiro, A. Zisserman, Non-local sparse models for image restoration. in Proceedings of IEEE International Conference on Computer Vision, pp. 2272–2279
(2009)
39. F. Luisier, T. Blu, M. Unser, A new SURE approach to image denoising: Interscale orthonormal
wavelet thresholding. IEEE Trans. Image Process. 16, 1057–7149 (2007)
40. K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising by sparse 3-D transformdomain collaborative filtering. IEEE Trans. Image Process. 16, 2080–2095 (2007)
41. P. Thévenaz, U.E. Ruttimann, M. Unser, A pyramid approach to subpixel registration based on
intensity. IEEE Trans. Image Process. 7, 27–41 (1998)
42. G. Cincotti, G. Loi, M. Pappalardo, Frequency decomposition and compounding of ultrasound
medical images with wavelets packets. IEEE Trans. Med. Imag. 20, 764–771 (2001)
43. P. Bao, L. Zhang, Noise reduction for magnetic resonance images via adaptive multiscale
products thresholding. IEEE Trans. Med. Imag. 22, 1089–1099 (2003)
44. S.J. Chiu, X.T. Li, P. Nicholas, C.A. Toth, J.A. Izatt, S. Farsiu, Automatic segmentation of seven
retinal layers in SDOCT images congruent with expert manual segmentation. Opt. Express 18,
19413–19428 (2010)
45. R. Zeyde, M. Elad, M. Protter, On single image scale-up using sparse-representations. in Curves
Surfaces, pp. 711–730 (2012)
46. K.S. Ni, T.Q. Nguyen, Image superresolution using support vector regression. IEEE Trans.
Image Process. 16, 1596–1610 (2007)
47. A.W. Scott, S. Farsiu, L.B. Enyedi, D.K. Wallace, C.A. Toth, Imaging the infant retina with
a hand-held spectral-domain optical coherence tomography device. Am. J. Ophthalmol. 147,
364–373 (2009)
L. Fang and S. Li
24. S. Chen, H. Liu, Z. Hu, H. Zhang, P. Shi, Y. Chen, Simultaneous reconstruction and segmentation of dynamic pet via low-rank and sparse matrix decomposition. IEEE Trans. Biomed. Eng.
62, 1784–1795 (2015)
25. M. Aharon, M. Elad, A.M. Bruckstein, The K-SVD: an algorithm for designing of overcomplete
dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311–4322 (2006)
26. L. Fang, S. Li, X. Kang, J.A. Izatt, S. Farsiu, 3-D Adaptive sparsity based image compression
with applications to optical coherence tomography. IEEE Trans. Med. Imag. 34, 1306–1320
(2015)
27. R. Kafieh, H. Rabbani, I. Selesnick, Three dimensional data-driven multi scale atomic representation of optical coherence tomography. IEEE Trans. Med. Imag. 34, 1042–1062 (2015)
28. M. Elad, M. Aharon, Image denoising via sparse and redundant representations over learned
dictionaries. IEEE Trans. Image Process. 15, 3736–3745 (2006)
29. J. Yang, J. Wright, T.S. Huang, Y. Ma, Image super-resolution via sparse representation. IEEE
Trans. Image Process. 19, 2861–2873 (2010)
30. O. Bryt, M. Elad, Compression of facial images using the K-SVD algorithm. J. Vis. Commun.
Image Represent. 19, 270–282 (2008)
31. J. Zepeda, C. Guillemot, E. Kijak, Image compression using sparse representations and the
iteration-tuned and aligned dictionary. IEEE J. Sel. Topics Signal Process. 5, 1061–1073 (2011)
32. K. Skretting, K. Engan, Image compression using learned dictionaries by RLS-DLA and compared with K-SVD. in Proceedings of IEEE International Conference on Acoustics Speech
Signal Processing, pp. 1517–1520 (2011)
33. A. Foi, Noise estimation and removal in MR imaging. in Proceeding of IEEE International
Symposium Biomedical Imaging (2011), pp. 1809–1814
34. S.G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal
Process. 41, 3397–3415 (1993)
35. P. Chatterjee, P. Milanfar, Clustering-based denoising with locally learned dictionaries. IEEE
Trans. Image Process. 18, 1438–1451 (2009)
36. W. Dong, L. Zhang, G. Shi, X. Wu, Image deblurring and super-resolution by adaptive sparse
domain selection and adaptive regularization. IEEE Trans. Image Process. 20, 1838–1857
(2011)
37. W. Dong, L. Zhang, G. Shi, Centralized sparse representation for image restoration. in IEEE
International Conference on Computer Vision, pp. 1259–1266 (2011)
38. J. Mairal, F. Bach, J. Ponce, G. Sapiro, A. Zisserman, Non-local sparse models for image restoration. in Proceedings of IEEE International Conference on Computer Vision, pp. 2272–2279
(2009)
39. F. Luisier, T. Blu, M. Unser, A new SURE approach to image denoising: Interscale orthonormal
wavelet thresholding. IEEE Trans. Image Process. 16, 1057–7149 (2007)
40. K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising by sparse 3-D transformdomain collaborative filtering. IEEE Trans. Image Process. 16, 2080–2095 (2007)
41. P. Thévenaz, U.E. Ruttimann, M. Unser, A pyramid approach to subpixel registration based on
intensity. IEEE Trans. Image Process. 7, 27–41 (1998)
42. G. Cincotti, G. Loi, M. Pappalardo, Frequency decomposition and compounding of ultrasound
medical images with wavelets packets. IEEE Trans. Med. Imag. 20, 764–771 (2001)
43. P. Bao, L. Zhang, Noise reduction for magnetic resonance images via adaptive multiscale
products thresholding. IEEE Trans. Med. Imag. 22, 1089–1099 (2003)
44. S.J. Chiu, X.T. Li, P. Nicholas, C.A. Toth, J.A. Izatt, S. Farsiu, Automatic segmentation of seven
retinal layers in SDOCT images congruent with expert manual segmentation. Opt. Express 18,
19413–19428 (2010)
45. R. Zeyde, M. Elad, M. Protter, On single image scale-up using sparse-representations. in Curves
Surfaces, pp. 711–730 (2012)
46. K.S. Ni, T.Q. Nguyen, Image superresolution using support vector regression. IEEE Trans.
Image Process. 16, 1596–1610 (2007)
47. A.W. Scott, S. Farsiu, L.B. Enyedi, D.K. Wallace, C.A. Toth, Imaging the infant retina with
a hand-held spectral-domain optical coherence tomography device. Am. J. Ophthalmol. 147,
364–373 (2009)