Abstract
In this paper, we consider a coupled system of partial differential equations (PDEs) based model for image restoration. Both the image and the edge variables are incorporated by coupling them into two different PDEs. It is shown that the initial-boundary value problem has global in time dissipative solutions (in a sense going back to P.-L. Lions), and several properties of these solutions are established. Some numerical examples are given to highlight the denoising nature of the proposed model along with some comparison results.
Similar content being viewed by others
Notes
Unfortunately there is no universal guideline for choosing parameters in diffusion based schemes and maximum PSNR based selection is done by sweeping the parameter set thoroughly. The important parameter σ in smoothing kernel G σ is set σ=2 for all the schemes and experiments reported here. This parameter needs to be increased if the noise level σ n is higher.
Code available at http://ece.uwaterloo.ca/~z70wang/research/ssim/.
Image courtesy of J. Portilla and available online at http://decsai.ugr.es/~javier/denoise/barbara.png.
Image courtesy of MIT.
Available at http://sipi.usc.edu/database/.
Image courtesy of UCF CVPR Group and available online at http://marathon.csee.usf.edu/edge/edge_detection.html.
Implemented using the MATLAB command edge(u 0, ‘canny’, σ).
References
Kornprobst, P., Deriche, R., Aubert, G.: Image sequence analysis via partial differential equations. J. Math. Imaging Vis. 11(1), 5–26 (1999). doi:10.1023/A:1008318126505
Sochen, N., Kimmel, R., Bruckstein, A.M.: Diffusions and confusions in signal and image processing. J. Math. Imaging Vis. 14(3), 195–209 (2001). doi:10.1023/A:1011277827470
Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing: Partial Differential Equation and Calculus of Variations. Springer, New York (2006)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990). doi:10.1109/34.56205
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1–4), 259–268 (1992). doi:10.1016/0167-2789(92)90242-F
Chambolle, A., Lions, P.L.: Image recovery via total variation minimization and related problems. Numer. Math. 76(2), 167–188 (1997). doi:10.1007/s002110050258
Tsai, Y.-H.R., Osher, S.: Total variation and level set methods in image science. Acta Numer. 14, 509–573 (2005). doi:10.1017/S0962492904000273
Goldstein, T., Osher, S.: The split Bregman algorithm for L1 regularized problems. SIAM J. Imaging Sci. 2(2), 323–343 (2009). doi:10.1137/080725891
Osher, S., Rudin, L.: Feature-oriented image enhancement using shock filters. SIAM J. Numer. Anal. 27(4), 919–940 (1990). doi:10.1137/0727053
Alveraz, L., Mazzora, L.: Signal and image restoration using shock filters and anisotropic diffusion. SIAM J. Numer. Anal. 31(2), 590–605 (1994). doi:10.1137/0731032
Wei, G.W.: Generalized Perona–Malik equation for image restoration. IEEE Signal Process. Lett. 6(7), 165–167 (1999). doi:10.1109/97.769359
You, Y.-L., Kaveh, M.: Fourth–order partial differential equation for noise removal. IEEE Trans. Image Process. 9, 1723–1730 (2000). doi:10.1109/83.869184
Chan, T., Marquina, A., Mulet, P.: High-order total variation-based image restoration. SIAM J. Sci. Comput. 22(2), 503–516 (2000). doi:10.1137/S1064827598344169
Lysaker, M., Lundervold, A., Tai, X.C.: Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Trans. Image Process. 12(12), 1579–1590 (2003). doi:10.1109/TIP.2003.819229
Greer, J.B., Bertozzi, A.L.: Traveling wave solutions of fourth order PDEs for image processing. SIAM J. Math. Anal. 36(1), 36–68 (2004). doi:10.1137/S0036141003427373
Rajan, J., Kannan, K., Kaimal, M.R.: An improved hybrid model for molecular image denoising. J. Math. Imaging Vis. 31(1), 73–79 (2008). doi:10.1007/s10851-008-0067-4
Didas, S., Weickert, J., Burgeth, B.: Properties of higher order nonlinear diffusion filtering. J. Math. Imaging Vis. 35(3), 208–226 (2009). doi:10.1007/s10851-009-0166-x
Guidotti, P., Longo, K.: Two enhanced fourth order diffusion models for image denoising. J. Math. Imaging Vis. 40(2), 188–198 (2011). doi:10.1007/s10851-010-0256-9
Blomgren, P., Chan, T.F., Mulet, P., Wang, C.K.: Total variation image restoration: numerical methods and extensions. In: IEEE International Conference on Image Processing (ICIP), Santa Barbara, CA, USA, pp. 384–387 (1997). doi:10.1109/ICIP.1997.632128
Lysaker, M., Tai, X.C.: Iterative image restoration combining total variation minimization and a second-order functional. Int. J. Comput. Vis. 66(1), 5–18 (2006). doi:10.1007/s11263-005-3219-7
Li, F., Shen, C., Fan, J., Shen, C.: Image restoration combining a total variational filter and a fourth-order filter. J. Vis. Commun. Image Represent. 18(4), 322–330 (2007). doi:10.1016/j.jvcir.2007.04.005
Cao, Y., Yin, J., Liu, Q., Li, M.: A class of nonlinear parabolic-hyperbolic equations applied to image restoration. Nonlinear Anal., Real World Appl. 11(1), 253–261 (2010). doi:10.1016/j.nonrwa.2008.11.004
Tadmor, E., Athavale, P.: Multiscale image representation using novel integro-differential equations. Inverse Probl. Theor. Imaging 3(4), 693–710 (2009). doi:10.3934/ipi.2009.3.693
Guidotti, P.: A new nonlocal nonlinear diffusion of image processing. J. Differ. Equ. 246(12), 4731–4742 (2009). doi:10.1016/j.jde.2009.03.017
Guidotti, P.: A new well-posed nonlinear nonlocal diffusion. Nonlinear Anal. 72(12), 4625–4637 (2010). doi:10.1016/j.na.2010.02.040
Guidotti, P., Lambers, J.: Two new nonlinear nonlocal diffusions for noise reduction. J. Math. Imaging Vis. 33(1), 25–37 (2009). doi:10.1007/s10851-008-0108-z
Janev, M., Pilipovic, S., Atanackovic, T., Obradovic, R., Ralevic, N.: Fully fractional anisotropic diffusion for image denoising. Math. Comput. Model. 54(1–2), 729–741 (2011). doi:10.1016/j.mcm.2011.03.017
Catte, V., Lions, P.L., Morel, J.-M., Coll, T.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 29(1), 182–193 (1992). doi:10.1137/0729012
Chen, Y., Wunderli, T.: Adaptive total variation for image restoration in BV space. J. Math. Anal. Appl. 272(3), 117–137 (2002). doi:10.1016/S0022-247X(02)00141-5
Shi, Y., Chang, Q.: New time dependent model for image restoration. Appl. Math. Comput. 179(1), 121–134 (2006). doi:10.1016/j.amc.2005.11.085
Barbu, T., Barbu, V., Biga, V., Coca, D.: A PDE variational approach to image denoising and restoration. Nonlinear Anal., Real World Appl. 10(3), 1351–1361 (2009). doi:10.1016/j.nonrwa.2008.01.017
Prasath, V.B.S., Singh, A.: Well-posed inhomogeneous nonlinear diffusion scheme for digital image denoising. J. Appl. Math. 2010, 763847 (2010), 14 pp. doi:10.1155/2010/763847
Prasath, V.B.S., Singh, A.: An adaptive anisotropic diffusion scheme for image restoration and selective smoothing. Int. J. Image Graph. 12(1), 18 (2012). doi:10.1142/S0219467812500039
Nitzberg, M., Shiota, T.: Nonlinear image filtering with edge and corner enhancement. IEEE Trans. Pattern Anal. Mach. Intell. 14(8), 826–833 (1992). doi:10.1109/34.149593
Teboul, S., Blane, L., Aubert, G., Barlaud, M.: Variational approach for edge preserving regularization using coupled PDEs. IEEE Trans. Image Process. 7(3), 387–397 (1998). doi:10.1109/83.661189
Cottet, G.-H., El Ayyadi, M.: A Volterra type model for image processing. IEEE Trans. Image Process. 7(3), 292–303 (1998). doi:10.1109/83.661179
Caselles, V., Sapiro, G., Chung, D.H.: Vector median filters, inf-sup operations, and coupled PDE’s: theoretical connections. J. Math. Imaging Vis. 12(2), 109–119 (2000). doi:10.1023/A:1008310305351
Chen, Y., Barcelos, C.A.Z., Mair, B.A.: Smoothing and edge detection by time-varying coupled nonlinear diffusion equations. Comput. Vis. Image Underst. 82(2), 85–100 (2001). doi:10.1006/cviu.2001.0903
Chen, Y., Bose, P.: On the incorporation of time-delay regularization into curvature-based diffusion. J. Math. Imaging Vis. 14(2), 149–164 (2001). doi:10.1023/A:1011211315825
Wei, G.W., Jia, Y.Q.: Synchronization-based image edge detection. Europhys. Lett. 59(6), 814–819 (2002). doi:10.1209/epl/i2002-00115-8
Chen, Y., Levine, S.E.: Image recovery via diffusion tensor and time-delay regularization. J. Vis. Commun. Image Represent. 13(1–2), 156–175 (2002). doi:10.1006/jvci.2001.0497
Belahmidi, A., Chambolle, A.: Time-delay regularization of anisotropic diffusion and image processing. Math. Model. Numer. Anal. 39(2), 231–251 (2005). doi:10.1051/m2an:2005010
Luo, H., Zhu, L., Ding, H.: Coupled anisotropic diffusion for image selective smoothing. Signal Process. 86, 1728–1736 (2006). doi:10.1016/j.sigpro.2005.09.019
Amann, H.: Time-delayed Perona-Malik type problems. Acta Math. Univ. Comen. LXXVI(1), 15–38 (2007)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721–741 (1984). doi:10.1109/TPAMI.1984.4767596
Charbonnier, P., Blanc-Feraud, L., Aubert, G., Barlaud, M.: Deterministic edge-preserving regularization in computed imaging. IEEE Trans. Image Process. 6(2), 298–311 (1997). doi:10.1109/83.551699
Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42(5), 577–685 (1989). doi:10.1002/cpa.3160420503
Ambrosio, L., Tortorelli, V.M.: Approximation of functionals depending on jumps by elliptic functionals via Γ-convergence. Commun. Pure Appl. Math. 43(8), 999–1036 (1990). doi:10.1002/cpa.3160430805
Erdem, E., Tari, S.: Mumford-Shah regularizer with contextual feedback. J. Math. Imaging Vis. 33(1), 67–84 (2009). doi:10.1007/s10851-008-0109-y
Ceccarelli, M.: A finite Markov random field approach to fast edge-preserving image recovery. Image Vis. Comput. 25(6), 792–804 (2007). doi:10.1016/j.imavis.2006.05.021
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tapen, M., Rother, C.: A comparative study of energy minimization methods for Markov random fields with smoothness based priors. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 1068–1080 (2008). doi:10.1109/TPAMI.2007.70844
Lions, P.-L.: Incompressible models. In: Mathematical Topics in Fluid Mechanics, vol. 1. Oxford Lecture Series in Mathematics and Its Applications. Clarendon, Oxford (1996)
Lions, P.-L.: Compactness in Boltzmann’s equation via Fourier integral operators and applications. I, II. J. Math. Kyoto Univ. 34(2), 391–427, 429–461 (1994)
Gamba, I.M., Panferov, V., Villani, C.: Upper Maxwellian bounds for the spatially homogeneous Boltzmann equation. Arch. Ration. Mech. Anal. 194(1), 253–282 (2009). doi:10.1007/s00205-009-0250-9
Wu, J.: Analytic results related to magneto-hydrodynamic turbulence. Physica D 136(3–4), 353–372 (2000). doi:10.1016/S0167-2789(99)00158-X
Arsenio, D., Saint-Raymond, L.: Maxwell’s equations and the Lorentz force (2011). www.math.univ-toulouse.fr/berestycki2011/Talks/Saint-Raymond.pdf
Vorotnikov, D.: Global generalized solutions for Maxwell-alpha and Euler-alpha equations. Nonlinearity 25(2), 309–327 (2012). doi:10.1088/0951-7715/25/2/309
Vorotnikov, D.A.: Dissipative solutions for equations of viscoelastic diffusion in polymers. J. Math. Anal. Appl. 339(2), 876–888 (2008). doi:10.1016/j.jmaa.2007.07.048
Koenderink, J.J.: The structure of images. Biol. Cybern. 50(5), 363–370 (1984). doi:10.1007/BF00336961
Gilboa, G., Sochen, N., Zeevi, Y.Y.: Variational denoising of partly textured images by spatially varying constraints. IEEE Trans. Image Process. 15(8), 2281–2289 (2006). doi:10.1109/TIP.2006.875247
Nordstrom, K.N.: Biased anisotropic diffusion: a unified regularization and diffusion approach to edge detection. Image Vis. Comput. 8(4), 318–327 (1990). doi:10.1016/0262-8856(90)80008-H
Adams, R.: Sobolev Spaces. Pure and Applied Mathematics, vol. 65. Academic Press, New York (1975)
Belahmidi, A.: Solvability of a coupled system arising in image and signal processing. Afr. Diaspora J. Math. 3(1), 45–61 (2005)
Temam, R.: Navier-Stokes Equations, Revised Edn.. Studies in Mathematics and Its Applications, vol. 2. North-Holland, Amsterdam (1979). Theory and numerical analysis, with an appendix by F. Thomasset
Zvyagin, V.G., Vorotnikov, D.A.: Topological Approximation Methods for Evolutionary Problems of Nonlinear Hydrodynamics. de Gruyter Series in Nonlinear Analysis and Applications, vol. 12. de Gruyter, Berlin (2008)
Simon, J.: Compact sets in the space L p(0,T;B). Ann Mat. Pura Appl. 146, 65–96 (1987). doi:10.1007/BF01762360
Krasnosel’skii, M.A.: Topological Methods in the Theory of Nonlinear Integral Equations. Pergamon, Elmsford (1964). Translated by A. H. Armstrong; translation edited by J. Burlak
Evans, L.C.: Partial Differential Equations, 2nd edn. Graduate Studies in Mathematics, vol. 19. Am. Math. Soc., Providence (2010)
Weickert, J., Romeny, B.M.H., Viergever, M.A.: Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans. Image Process. 7(3), 398–410 (1998). doi:10.1109/83.661190
Li, X., Chen, T.: Nonlinear diffusion with multiple edginess thresholds. Pattern Recognit. 27(8), 1029–1037 (1994). doi:10.1016/0031-3203(94)90142-2
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). doi:10.1109/TIP.2003.819861. http://ece.uwaterloo.ca/~z70wang/research/ssim/
Dong, Y., Hintermuller, M., Rincon-Camacho, M.M.: Automated regularization parameter selection in multi-scale total variation models for image restoration. J. Math. Imaging Vis. 40(1), 82–104 (2011). doi:10.1007/s10851-010-0248-9
Buades, A., Coll, B., Morel, J.M.: A review of image denoising methods, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2006). doi:10.1137/040616024
Pizarro, L., Mrazek, P., Didas, S., Grewenig, S., Weickert, J.: Generalised nonlocal image smoothing. Int. J. Comput. Vis. 90(1), 62–87 (2010). doi:10.1007/s11263-010-0337-7
Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986). doi:10.1109/TPAMI.1986.4767851
Aujol, J.-F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decomposition—modeling, algorithms and parameter selection. Int. J. Comput. Vis. 67(1), 111–136 (2006). doi:10.1007/s11263-006-4331-z
Acknowledgements
The first author would like to thank the Fields Institute, Toronto, Canada for their great hospitality during the work on this article. The authors are grateful to the referees for the constructive remarks which led to improvements in this work.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Prasath, V.B.S., Vorotnikov, D. On a System of Adaptive Coupled PDEs for Image Restoration. J Math Imaging Vis 48, 35–52 (2014). https://doi.org/10.1007/s10851-012-0386-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10851-012-0386-3