M. Stampanoni, Z. Wang, T. Thring, C. David, E. Roessl, M. Trippel, R. Kubik-Huch, G. Singer, M. K. Hohl, and N. Hauser, “The first analysis and clinical evaluation of native breast tissue using differential phase-contrast mmmography,” Invest. Radiol. 46(12), 801–806 (2011).

[CrossRef]
[PubMed]

S. K. Mohideen, S. A. Perumal, and M. M Sathik, “Image denoising using discrete wavelet transform,” J. Comput. Sci. 8(1), 8–11 (2011).

V. Revol, C. Kottler, R. Kaufmann, U. Straumann, and C. Urban, “Noise analysis of grating-based x-ray differential phase contrast imaging,” Rev. Sci. Instrum. 81,073709 (2010).

[CrossRef]
[PubMed]

D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (2009).

[CrossRef]

M. P. Sampat, Z. Wang, S. Gupta, A. C. Bovik, and M. K. Markey, “Complex wavelet structural similarity: a new image similarity index,” IEEE Trans. Image Process. 18(11), 2385–2401 (2009).

[CrossRef]
[PubMed]

Z. Wang and A. C. Bovik, “Mean-square error : Love it or leave it?,” IEEE Signal Process Mag. 26(1), 98–117 (2009).

[CrossRef]

J.F. Aujol and G. Gilboa, “Constrained and SNR-based solutions for TV-Hilbert space image denoising,” J. Math. Imaging Vision 26,217–237 (2006).

[CrossRef]

P. Sakellaropoulos, L. Costaridou, and G. Panayiotakis, “A wavelet-based spatially adaptive method for mammographic contrast enhancement,” Phys. Med. Biol. 48(6), 787–803 (2003).

[CrossRef]
[PubMed]

J. Kaufhold, J. A. Thomas, W. Eberhard, C. E. Galbo, and D. E. Trotter, “A calibration approach to glandular tissue composition estimation in digital mammography,” Med. Phys. 29,1867–1880 (2002).

[CrossRef]
[PubMed]

S. G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising,” IEEE Trans. Image Process. 9(9), 1522–1531 (2000).

[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]

B.M. Priestley, “Evolutionary spectra and non-stationary processes,” J. R. Stat. Soc. Series B 27(2), 204–237 (1965).

Y. Jin, E. Angelini, and A. Laine, “Wavelets in medical image processing: denoising, segmentation and registration,” in Handbook of Biomedical Image Analysis, Volume 1: Segmentation models, Part A, J. S. Suri, D.L. Wilson, and S. Laxminarayan, eds. (Kluwer Academic/Plenum Publishers, 2005), pp. 305–358.

[CrossRef]

J.F. Aujol and G. Gilboa, “Constrained and SNR-based solutions for TV-Hilbert space image denoising,” J. Math. Imaging Vision 26,217–237 (2006).

[CrossRef]

M. P. Sampat, Z. Wang, S. Gupta, A. C. Bovik, and M. K. Markey, “Complex wavelet structural similarity: a new image similarity index,” IEEE Trans. Image Process. 18(11), 2385–2401 (2009).

[CrossRef]
[PubMed]

Z. Wang and A. C. Bovik, “Mean-square error : Love it or leave it?,” IEEE Signal Process Mag. 26(1), 98–117 (2009).

[CrossRef]

G. Y. Chen, T. D. Bui, and A. Krzyzak, “Image denoising using neighbouring wavelet coefficients,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2004), pp. 917–920.

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]

S. G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising,” IEEE Trans. Image Process. 9(9), 1522–1531 (2000).

[CrossRef]

G. Y. Chen, T. D. Bui, and A. Krzyzak, “Image denoising using neighbouring wavelet coefficients,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2004), pp. 917–920.

P. Sakellaropoulos, L. Costaridou, and G. Panayiotakis, “A wavelet-based spatially adaptive method for mammographic contrast enhancement,” Phys. Med. Biol. 48(6), 787–803 (2003).

[CrossRef]
[PubMed]

M. Stampanoni, Z. Wang, T. Thring, C. David, E. Roessl, M. Trippel, R. Kubik-Huch, G. Singer, M. K. Hohl, and N. Hauser, “The first analysis and clinical evaluation of native breast tissue using differential phase-contrast mmmography,” Invest. Radiol. 46(12), 801–806 (2011).

[CrossRef]
[PubMed]

D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (2009).

[CrossRef]

J. Kaufhold, J. A. Thomas, W. Eberhard, C. E. Galbo, and D. E. Trotter, “A calibration approach to glandular tissue composition estimation in digital mammography,” Med. Phys. 29,1867–1880 (2002).

[CrossRef]
[PubMed]

J. Kaufhold, J. A. Thomas, W. Eberhard, C. E. Galbo, and D. E. Trotter, “A calibration approach to glandular tissue composition estimation in digital mammography,” Med. Phys. 29,1867–1880 (2002).

[CrossRef]
[PubMed]

J.F. Aujol and G. Gilboa, “Constrained and SNR-based solutions for TV-Hilbert space image denoising,” J. Math. Imaging Vision 26,217–237 (2006).

[CrossRef]

B. Goossens, A. Pizurica, and W. Philips, “Em-based estimation of spatially variant correlated image noise,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2008), pp. 1744–1747.

M. P. Sampat, Z. Wang, S. Gupta, A. C. Bovik, and M. K. Markey, “Complex wavelet structural similarity: a new image similarity index,” IEEE Trans. Image Process. 18(11), 2385–2401 (2009).

[CrossRef]
[PubMed]

M. Stampanoni, Z. Wang, T. Thring, C. David, E. Roessl, M. Trippel, R. Kubik-Huch, G. Singer, M. K. Hohl, and N. Hauser, “The first analysis and clinical evaluation of native breast tissue using differential phase-contrast mmmography,” Invest. Radiol. 46(12), 801–806 (2011).

[CrossRef]
[PubMed]

M. Stampanoni, Z. Wang, T. Thring, C. David, E. Roessl, M. Trippel, R. Kubik-Huch, G. Singer, M. K. Hohl, and N. Hauser, “The first analysis and clinical evaluation of native breast tissue using differential phase-contrast mmmography,” Invest. Radiol. 46(12), 801–806 (2011).

[CrossRef]
[PubMed]

E. Jerhotova, J. Svihlik, and A. Prochazka, “Biomedical image volumes denoising via the wavelet transform,” in Applied Biomedical Engineering, G.D. Gargiulo and A. McEwan, eds. (Intech, 2011), pp 435–458 (2009).

Y. Jin, E. Angelini, and A. Laine, “Wavelets in medical image processing: denoising, segmentation and registration,” in Handbook of Biomedical Image Analysis, Volume 1: Segmentation models, Part A, J. S. Suri, D.L. Wilson, and S. Laxminarayan, eds. (Kluwer Academic/Plenum Publishers, 2005), pp. 305–358.

[CrossRef]

D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (2009).

[CrossRef]

J. Kaufhold, J. A. Thomas, W. Eberhard, C. E. Galbo, and D. E. Trotter, “A calibration approach to glandular tissue composition estimation in digital mammography,” Med. Phys. 29,1867–1880 (2002).

[CrossRef]
[PubMed]

V. Revol, C. Kottler, R. Kaufmann, U. Straumann, and C. Urban, “Noise analysis of grating-based x-ray differential phase contrast imaging,” Rev. Sci. Instrum. 81,073709 (2010).

[CrossRef]
[PubMed]

P. Kenterlis and D. Salonikidis, “Evaluation of wavelet domain methods for image denoising,” (technical report).

P. Kisilev, D. Shaked, and S. H. Lim, “Noise and signal activity maps for better imaging algorithms,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2007), pp.117–120.

V. Revol, C. Kottler, R. Kaufmann, U. Straumann, and C. Urban, “Noise analysis of grating-based x-ray differential phase contrast imaging,” Rev. Sci. Instrum. 81,073709 (2010).

[CrossRef]
[PubMed]

G. Y. Chen, T. D. Bui, and A. Krzyzak, “Image denoising using neighbouring wavelet coefficients,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2004), pp. 917–920.

M. Stampanoni, Z. Wang, T. Thring, C. David, E. Roessl, M. Trippel, R. Kubik-Huch, G. Singer, M. K. Hohl, and N. Hauser, “The first analysis and clinical evaluation of native breast tissue using differential phase-contrast mmmography,” Invest. Radiol. 46(12), 801–806 (2011).

[CrossRef]
[PubMed]

Y. Jin, E. Angelini, and A. Laine, “Wavelets in medical image processing: denoising, segmentation and registration,” in Handbook of Biomedical Image Analysis, Volume 1: Segmentation models, Part A, J. S. Suri, D.L. Wilson, and S. Laxminarayan, eds. (Kluwer Academic/Plenum Publishers, 2005), pp. 305–358.

[CrossRef]

P. Kisilev, D. Shaked, and S. H. Lim, “Noise and signal activity maps for better imaging algorithms,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2007), pp.117–120.

M. P. Sampat, Z. Wang, S. Gupta, A. C. Bovik, and M. K. Markey, “Complex wavelet structural similarity: a new image similarity index,” IEEE Trans. Image Process. 18(11), 2385–2401 (2009).

[CrossRef]
[PubMed]

S. K. Mohideen, S. A. Perumal, and M. M Sathik, “Image denoising using discrete wavelet transform,” J. Comput. Sci. 8(1), 8–11 (2011).

P. Sakellaropoulos, L. Costaridou, and G. Panayiotakis, “A wavelet-based spatially adaptive method for mammographic contrast enhancement,” Phys. Med. Biol. 48(6), 787–803 (2003).

[CrossRef]
[PubMed]

S. K. Mohideen, S. A. Perumal, and M. M Sathik, “Image denoising using discrete wavelet transform,” J. Comput. Sci. 8(1), 8–11 (2011).

B. Goossens, A. Pizurica, and W. Philips, “Em-based estimation of spatially variant correlated image noise,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2008), pp. 1744–1747.

B. Goossens, A. Pizurica, and W. Philips, “Em-based estimation of spatially variant correlated image noise,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2008), pp. 1744–1747.

B.M. Priestley, “Evolutionary spectra and non-stationary processes,” J. R. Stat. Soc. Series B 27(2), 204–237 (1965).

E. Jerhotova, J. Svihlik, and A. Prochazka, “Biomedical image volumes denoising via the wavelet transform,” in Applied Biomedical Engineering, G.D. Gargiulo and A. McEwan, eds. (Intech, 2011), pp 435–458 (2009).

S. Rangarajan, R. Venkataramanan, and R. Shah, “Image denoising using wavelets,” (technical report, 2002).

V. Revol, C. Kottler, R. Kaufmann, U. Straumann, and C. Urban, “Noise analysis of grating-based x-ray differential phase contrast imaging,” Rev. Sci. Instrum. 81,073709 (2010).

[CrossRef]
[PubMed]

M. Stampanoni, Z. Wang, T. Thring, C. David, E. Roessl, M. Trippel, R. Kubik-Huch, G. Singer, M. K. Hohl, and N. Hauser, “The first analysis and clinical evaluation of native breast tissue using differential phase-contrast mmmography,” Invest. Radiol. 46(12), 801–806 (2011).

[CrossRef]
[PubMed]

P. Sakellaropoulos, L. Costaridou, and G. Panayiotakis, “A wavelet-based spatially adaptive method for mammographic contrast enhancement,” Phys. Med. Biol. 48(6), 787–803 (2003).

[CrossRef]
[PubMed]

P. Kenterlis and D. Salonikidis, “Evaluation of wavelet domain methods for image denoising,” (technical report).

M. P. Sampat, Z. Wang, S. Gupta, A. C. Bovik, and M. K. Markey, “Complex wavelet structural similarity: a new image similarity index,” IEEE Trans. Image Process. 18(11), 2385–2401 (2009).

[CrossRef]
[PubMed]

S. K. Mohideen, S. A. Perumal, and M. M Sathik, “Image denoising using discrete wavelet transform,” J. Comput. Sci. 8(1), 8–11 (2011).

S. Rangarajan, R. Venkataramanan, and R. Shah, “Image denoising using wavelets,” (technical report, 2002).

P. Kisilev, D. Shaked, and S. H. Lim, “Noise and signal activity maps for better imaging algorithms,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2007), pp.117–120.

M. Stampanoni, Z. Wang, T. Thring, C. David, E. Roessl, M. Trippel, R. Kubik-Huch, G. Singer, M. K. Hohl, and N. Hauser, “The first analysis and clinical evaluation of native breast tissue using differential phase-contrast mmmography,” Invest. Radiol. 46(12), 801–806 (2011).

[CrossRef]
[PubMed]

M. Stampanoni, Z. Wang, T. Thring, C. David, E. Roessl, M. Trippel, R. Kubik-Huch, G. Singer, M. K. Hohl, and N. Hauser, “The first analysis and clinical evaluation of native breast tissue using differential phase-contrast mmmography,” Invest. Radiol. 46(12), 801–806 (2011).

[CrossRef]
[PubMed]

V. Revol, C. Kottler, R. Kaufmann, U. Straumann, and C. Urban, “Noise analysis of grating-based x-ray differential phase contrast imaging,” Rev. Sci. Instrum. 81,073709 (2010).

[CrossRef]
[PubMed]

E. Jerhotova, J. Svihlik, and A. Prochazka, “Biomedical image volumes denoising via the wavelet transform,” in Applied Biomedical Engineering, G.D. Gargiulo and A. McEwan, eds. (Intech, 2011), pp 435–458 (2009).

J. Kaufhold, J. A. Thomas, W. Eberhard, C. E. Galbo, and D. E. Trotter, “A calibration approach to glandular tissue composition estimation in digital mammography,” Med. Phys. 29,1867–1880 (2002).

[CrossRef]
[PubMed]

M. Stampanoni, Z. Wang, T. Thring, C. David, E. Roessl, M. Trippel, R. Kubik-Huch, G. Singer, M. K. Hohl, and N. Hauser, “The first analysis and clinical evaluation of native breast tissue using differential phase-contrast mmmography,” Invest. Radiol. 46(12), 801–806 (2011).

[CrossRef]
[PubMed]

M. Stampanoni, Z. Wang, T. Thring, C. David, E. Roessl, M. Trippel, R. Kubik-Huch, G. Singer, M. K. Hohl, and N. Hauser, “The first analysis and clinical evaluation of native breast tissue using differential phase-contrast mmmography,” Invest. Radiol. 46(12), 801–806 (2011).

[CrossRef]
[PubMed]

J. Kaufhold, J. A. Thomas, W. Eberhard, C. E. Galbo, and D. E. Trotter, “A calibration approach to glandular tissue composition estimation in digital mammography,” Med. Phys. 29,1867–1880 (2002).

[CrossRef]
[PubMed]

V. Revol, C. Kottler, R. Kaufmann, U. Straumann, and C. Urban, “Noise analysis of grating-based x-ray differential phase contrast imaging,” Rev. Sci. Instrum. 81,073709 (2010).

[CrossRef]
[PubMed]

S. Rangarajan, R. Venkataramanan, and R. Shah, “Image denoising using wavelets,” (technical report, 2002).

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]

S. G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising,” IEEE Trans. Image Process. 9(9), 1522–1531 (2000).

[CrossRef]

M. Stampanoni, Z. Wang, T. Thring, C. David, E. Roessl, M. Trippel, R. Kubik-Huch, G. Singer, M. K. Hohl, and N. Hauser, “The first analysis and clinical evaluation of native breast tissue using differential phase-contrast mmmography,” Invest. Radiol. 46(12), 801–806 (2011).

[CrossRef]
[PubMed]

M. P. Sampat, Z. Wang, S. Gupta, A. C. Bovik, and M. K. Markey, “Complex wavelet structural similarity: a new image similarity index,” IEEE Trans. Image Process. 18(11), 2385–2401 (2009).

[CrossRef]
[PubMed]

Z. Wang and A. C. Bovik, “Mean-square error : Love it or leave it?,” IEEE Signal Process Mag. 26(1), 98–117 (2009).

[CrossRef]

S. G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising,” IEEE Trans. Image Process. 9(9), 1522–1531 (2000).

[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]

D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (2009).

[CrossRef]

Z. Wang and A. C. Bovik, “Mean-square error : Love it or leave it?,” IEEE Signal Process Mag. 26(1), 98–117 (2009).

[CrossRef]

S. G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising,” IEEE Trans. Image Process. 9(9), 1522–1531 (2000).

[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]

M. P. Sampat, Z. Wang, S. Gupta, A. C. Bovik, and M. K. Markey, “Complex wavelet structural similarity: a new image similarity index,” IEEE Trans. Image Process. 18(11), 2385–2401 (2009).

[CrossRef]
[PubMed]

M. Stampanoni, Z. Wang, T. Thring, C. David, E. Roessl, M. Trippel, R. Kubik-Huch, G. Singer, M. K. Hohl, and N. Hauser, “The first analysis and clinical evaluation of native breast tissue using differential phase-contrast mmmography,” Invest. Radiol. 46(12), 801–806 (2011).

[CrossRef]
[PubMed]

S. K. Mohideen, S. A. Perumal, and M. M Sathik, “Image denoising using discrete wavelet transform,” J. Comput. Sci. 8(1), 8–11 (2011).

J.F. Aujol and G. Gilboa, “Constrained and SNR-based solutions for TV-Hilbert space image denoising,” J. Math. Imaging Vision 26,217–237 (2006).

[CrossRef]

B.M. Priestley, “Evolutionary spectra and non-stationary processes,” J. R. Stat. Soc. Series B 27(2), 204–237 (1965).

J. Kaufhold, J. A. Thomas, W. Eberhard, C. E. Galbo, and D. E. Trotter, “A calibration approach to glandular tissue composition estimation in digital mammography,” Med. Phys. 29,1867–1880 (2002).

[CrossRef]
[PubMed]

P. Sakellaropoulos, L. Costaridou, and G. Panayiotakis, “A wavelet-based spatially adaptive method for mammographic contrast enhancement,” Phys. Med. Biol. 48(6), 787–803 (2003).

[CrossRef]
[PubMed]

V. Revol, C. Kottler, R. Kaufmann, U. Straumann, and C. Urban, “Noise analysis of grating-based x-ray differential phase contrast imaging,” Rev. Sci. Instrum. 81,073709 (2010).

[CrossRef]
[PubMed]

P. Kisilev, D. Shaked, and S. H. Lim, “Noise and signal activity maps for better imaging algorithms,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2007), pp.117–120.

B. Goossens, A. Pizurica, and W. Philips, “Em-based estimation of spatially variant correlated image noise,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2008), pp. 1744–1747.

PIHera Piezo Lineartisch manual PI. P-620.1 P-629.1.

G. Y. Chen, T. D. Bui, and A. Krzyzak, “Image denoising using neighbouring wavelet coefficients,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2004), pp. 917–920.

Y. Jin, E. Angelini, and A. Laine, “Wavelets in medical image processing: denoising, segmentation and registration,” in Handbook of Biomedical Image Analysis, Volume 1: Segmentation models, Part A, J. S. Suri, D.L. Wilson, and S. Laxminarayan, eds. (Kluwer Academic/Plenum Publishers, 2005), pp. 305–358.

[CrossRef]

E. Jerhotova, J. Svihlik, and A. Prochazka, “Biomedical image volumes denoising via the wavelet transform,” in Applied Biomedical Engineering, G.D. Gargiulo and A. McEwan, eds. (Intech, 2011), pp 435–458 (2009).

S. Rangarajan, R. Venkataramanan, and R. Shah, “Image denoising using wavelets,” (technical report, 2002).

P. Kenterlis and D. Salonikidis, “Evaluation of wavelet domain methods for image denoising,” (technical report).