Convergence analysis of tight framelet approach for missing data recovery
Tóm tắt
Từ khóa
Tài liệu tham khảo
Abreu, E., Lightstone, M., Mitra, S., Arakawa, K.: A new efficient approach for the removal of impulse noise from highly corrupted images. IEEE Trans. Image Process. 5(3), 1012–1025 (1996)
Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of SIGGRAPH, pp. 417–424. New Orleans, LA (2000)
Cai, J.-F., Chan, R., Di Fiore, C.: Minimization of a detail-preserving regularization functional for impulse noise removal. J. Math. Imaging Vision 29(1), 79–91 (2007)
Cai, J.-F., Chan, R., Shen, Z.: A framelet-based image inpainting algorithm. Appl. Comput. Harmon. Anal. 24(2), 131–149 (2008)
Chan, R., Chan, T., Shen, L., Shen, Z.: Wavelet algorithms for high-resolution image reconstruction. SIAM J. Sci. Comput. 24(4), 1408–1432 (2003)
Chan, R., Ho, C.-W., Nikolova, M.: Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Trans. Image Process. 14, 1479–1485 (2005)
Chan, R., Hu, C., Nikolova, M.: An iterative procedure for removing random-valued impulse noise. IEEE Signal Process. Lett. 11, 921–924 (2004)
Chan, R., Riemenschneider, S.D., Shen, L., Shen, Z.: Tight frame: the efficient way for high-resolution image reconstruction. Appl. Comput. Harmon. Anal. 17(1), 91–115 (2004)
Chan, R., Shen, L., Shen, Z.: A framelet-based approach for image in painting. Technical Report 2005-4. The Chinese University of Hong Kong, Feb. (2005)
Chan, T., Shen, J.: Nontexture inpainting by curvature driven diffusion (CDD). J. Visul Comm. Image Rep. 12, 436–449 (2001)
Chen, T., Wu, H.: Space variant median filters for the restoration of impulse noise corrupted images. IEEE Trans. Circuits and Systems II 48, 784–789 (2001)
Combettes, P., Wajs, V.: Signal recovery by proximal forward-backward splitting. Multiscale Model. Simul. 4, 1168–1200 (2005)
Daubechies, I.: Ten lectures on wavelets. CBMS Conference Series in Applied Mathematics, vol. 61, SIAM, Philadelphia (1992)
Donoho, D., Johnstone, I.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425–455 (1994)
Duffin, R., Schaeffer, A.: A class of nonharmonic Fourier series. Trans. Amer. Math. Soc. 72, 341–366 (1952)
Flaig, A., Arce, G., Barner, K.: Affine order statistics filters: “medianization” of linear FIR filters. IEEE Trans. Signal Process. 46, 2101–2112 (1998)
Gonzalez, R., Woods, R.: Digital Image Processing. Addison-Wesley, Boston, MA (1993)
Guleryuz, O.G.: Nonlinear approximation based image recovery using adaptive sparse reconstruction and iterated denoising: part I—theory. IEEE Trans. Image Process. 15(3), 539–554 (2006)
Guleryuz, O.G.: Nonlinear approximation based image recovery using adaptive sparse reconstruction and iterated denoising: part II—adaptive algorithms. IEEE Trans. Image Process. 15(3), 555–571 (2006)
Hwang, H., Haddad, R.: Adaptive median filters: new algorithms and results. IEEE Trans. Image Process. 4, 499–502 (1995)
Ko, S., Lee, Y.: Adaptive center weighted median filter. IEEE Trans. Circuits Syst. 38, 984–993 (1998)
Ng, M., Chan, R., Tang, W.: A fast algorithm for deblurring models with Neumann boundary conditions. SIAM J. Sci. Comput. 21, 851–866 (2000)
Nikolova, M.: A variational approach to remove outliers and impulse noise. J. Math. Imaging Vision 20, 99–120 (2004)
Pok, G., Liu, J.-C., Nair, A.S.: Selective removal of impulse noise based on homogeneity level information. IEEE Trans. Image Process. 12, 85–92 (2003)
Ron, A., Shen, Z.: Affine system in $L_2(R^d)$ : the analysis of the analysis operator. J. Funct. Anal. 148, 408–447 (1997)
Sun, T., Neuvo, Y.: Detail-preserving based filters in image processing. Pattern Recogn. Lett. 15, 341–347 (1994)
Yin, L., Yang, R., Gabbouj, M., Neuvo, Y.: Weighted median filters: a tutorial. IEEE Trans. Circuit Theory 41, 157–192 (1996)