Abstract
Image enhancement with forward-and-backward (FAB) diffusion lacks a sound theory and is numerically very challenging due to its diffusivities that are negative within a certain gradient range. In our paper, we address both problems. First we establish a comprehensive theory for space-discrete and time-continuous FAB diffusion processes. It requires approximating the gradient magnitude with a non-standard discretisation. Then, we show that this theory carries over to the fully discrete case, when an explicit time discretisation with a fairly restrictive step-size limit is applied. To come up with more efficient algorithms, we propose three accelerated schemes: (i) an explicit scheme with global time step size adaptation that is also well suited for parallel implementations on GPUs, (ii) a randomised two-pixel scheme that offers optimal adaptivity of the time step size, (iii) a deterministic two-pixel scheme which benefits from less restrictive consistency bounds. Our experiments demonstrate that these algorithms allow speed-ups by up to three orders of magnitude without compromising stability or introducing visual artefacts.







Similar content being viewed by others
References
Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, Applied Mathematical Sciences, vol. 147, 2nd edn. Springer, New York (2006)
Babaud, J., Witkin, A.P., Baudin, M., Duda, R.O.: Uniqueness of the Gaussian kernel for scale space filtering. IEEE Trans. Pattern Anal. Mach. Intell. 8, 26–33 (1986)
Breuß, M., Welk, M.: Staircasing in semidiscrete stabilised inverse diffusion algorithms. J. Comput. Appl. Math. 206(1), 520–533 (2007)
Burgeth, B., Weickert, J., Tari, S.: Minimally stochastic schemes for singular diffusion equations. In: Tai, X.C., Lie, K.A., Chan, T.F., Osher, S. (eds.) Image Processing Based on Partial Differential Equations, pp. 325–339. Springer, Berlin (2007)
Carasso, A.S.: Stable explicit time marching in well-posed or ill-posed nonlinear parabolic equations. Inverse Probl. Sci. Eng. 24(8), 1364–1384 (2016)
Carasso, A.S.: Stabilized Richardson leapfrog scheme in explicit stepwise computation of forward or backward nonlinear parabolic equations. Inverse Probl. Sci. Eng. 25(12), 1719–1742 (2017)
Elmoataz, A., Lezoray, O., Ta, V.T., Bougleux, S.: Partial difference equations on graphs for local and nonlocal image processing, Chap. 7. In: Lezoray, O., Grady, L. (eds.) Image Processing and Analysis with Graphs: Theory and Practice, pp. 174–206. CRC Press, Boca Raton (2012)
Gabor, D.: Information theory in electron microscopy. Lab. Investig. 14, 801–807 (1965)
Gilboa, G., Sochen, N., Zeevi, Y.Y.: Image sharpening by flows based on triple well potentials. J. Math. Imaging Vis. 20, 121–131 (2004)
Gilboa, G., Sochen, N.A., Zeevi, Y.Y.: Forward-and-backward diffusion processes for adaptive image enhancement and denoising. IEEE Trans. Image Process. 11(7), 689–703 (2002)
Iijima, T.: Basic theory on normalization of pattern (in case of typical one-dimensional pattern). Bull. Electrotech. Lab. 26, 368–388 (1962). In Japanese
Kovasznay, L.S.G., Joseph, H.M.: Image processing. Proc. IRE 43(5), 560–570 (1955)
Kramer, H.P., Bruckner, J.B.: Iterations of a non-linear transformation for enhancement of digital images. Pattern Recogn. 7, 53–58 (1975)
Lindeberg, T.: Scale-Space Theory in Computer Vision. Kluwer, Boston (1994)
Lindenbaum, M., Fischer, M., Bruckstein, A.: On Gabor’s contribution to image enhancement. Pattern Recogn. 27, 1–8 (1994)
Mehlhorn, K., Sanders, P.: Algorithms and Data Structures—The Basic Toolbox. Springer, Berlin (2008)
Mickens, R.E.: Nonstandard Finite Difference Models of Differential Equations. World Scientific, Singapore (1994)
Mrázek, P., Weickert, J., Steidl, G.: Diffusion-inspired shrinkage functions and stability results for wavelet denoising. Int. J. Comput. Vis. 64(2/3), 171–186 (2005)
Nikolova, M.: Local strong homogeneity of a regularized estimator. SIAM J. Appl. Math. 61(2), 633–658 (2000)
Nikolova, M.: Minimizers of cost-functions involving nonsmooth data fidelity terms. Application to the processing of outliers. SIAM J. Numer. Anal. 40(3), 965–994 (2002)
Osher, S., Rudin, L.: Shocks and other nonlinear filtering applied to image processing. In: Tescher, A.G. (ed.) Applications of Digital Image Processing XIV, Proceedings of SPIE, vol. 1567, pp. 414–431. SPIE Press, Bellingham (1991)
Osher, S., Rudin, L.I.: Feature-oriented image enhancement using shock filters. SIAM J. Numer. Anal. 27, 919–940 (1990)
Perko, L.: Differential Equations and Dynamical Systems, 3rd edn. Springer, New York (2001)
Perona, P., Malik, J.: Scale space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990)
Pollak, I., Willsky, A.S., Krim, H.: Image segmentation and edge enhancement with stabilized inverse diffusion equations. IEEE Trans. Image Process. 9(2), 256–266 (2000)
Smolka, B.: Combined forward and backward anisotropic diffusion filtering of color images. In: Van Gool, L. (ed.) Pattern Recognition. Lecture Notes in Computer Science, vol. 2449, pp. 314–320. Springer, Berlin (2002)
Smolka, B., Plataniotis, K.N.: On the coupled forward and backward anistropic diffusion scheme for color image enhancement. In: Lew, M.S., Sebe, N., Eakins, J.P. (eds.) Image and Video Retrieval. Lecture Notes in Computer Science, vol. 2383, pp. 70–80. Springer, Berlin (2002)
Steidl, G., Weickert, J., Brox, T., Mrázek, P., Welk, M.: On the equivalence of soft wavelet shrinkage, total variation diffusion, total variation regularization, and SIDEs. SIAM J. Numer. Anal. 42(2), 686–713 (2004)
Tikhonov, A.N., Arsenin, V.Y.: Solutions of Ill-Posed Problems. Wiley, Washington, DC (1977)
Wang, Y., Niu, R., Shen, H., Yu, X.: Forward-and-backward diffusion for hyperspectral remote sensing image smoothing and enhancement. In: Li, D., Gong, J., Wu, H. (eds.) International Conference on Earth Observation Data Processing and Analysis (ICEODPA), Proceedings of SPIE, vol. 7285. SPIE Press, Bellingham (2008)
Wang, Y., Niu, R., Zhang, L., Wu, K., Sahli, H.: A scale-based forward-and-backward diffusion process for adaptive image enhancement and denoising. EURASIP J. Adv. Signal Process. 2011, 22 (2011)
Wang, Y., Zhang, L., Li, P.: Local variance-controlled forward-and-backward diffusion for image enhancement and noise reduction. IEEE Trans. Image Process. 16(7), 1854–1864 (2007)
Weickert, J.: Anisotropic Diffusion in Image Processing. Teubner, Stuttgart (1998)
Weickert, J., Benhamouda, B.: A semidiscrete nonlinear scale-space theory and its relation to the Perona-Malik paradox. In: Solina, F., Kropatsch, W.G., Klette, R., Bajcsy, R. (eds.) Advances in Computer Vision, pp. 1–10. Springer, Wien (1997)
Welk, M., Gilboa, G., Weickert, J.: Theoretical foundations for discrete forward-and-backward diffusion filtering. In: Tai, X.C., Mørken, K., Lysaker, M., Lie, K.A. (eds.) Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, vol. 5567, pp. 527–538. Springer, Berlin (2009)
Welk, M., Steidl, G., Weickert, J.: Locally analytic schemes: a link between diffusion filtering and wavelet shrinkage. Appl. Comput. Harm. Anal. 24, 195–224 (2008)
Welk, M., Weickert, J.: An efficient and stable two-pixel scheme for 2D forward-and-backward diffusion. In: Lauze, F., Dong, Y., Dahl, A.B. (eds.) Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, vol. 10302, pp. 94–106. Springer, Cham (2017)
Welk, M., Weickert, J., Galić, I.: Theoretical foundations for spatially discrete 1-D shock filtering. Image Vis. Comput. 25(4), 455–463 (2007)
Witkin, A.P.: Scale-space filtering. In: Proceedings of Eighth International Joint Conference on Artificial Intelligence, vol. 2, pp. 945–951. Karlsruhe, West Germany (1983)
Zakeri, A., Jannati, Q., Amiri, A.: A numerical scheme for solving nonlinear backward parabolic problems. Bull. Iran. Math. Soc. 41(6), 1453–1464 (2015)
Acknowledgements
J.W. and G.G. would like to thank the Isaac Newton Institute for Mathematical Sciences for support and hospitality during the programme Variational Methods and Effective Algorithms for Imaging and Vision, when final work on this paper was undertaken. This work was supported by EPSRC Grant Number EP/K032208/1 and by a Rothschild Distinguished Visiting Fellowship for J.W.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Welk, M., Weickert, J. & Gilboa, G. A Discrete Theory and Efficient Algorithms for Forward-and-Backward Diffusion Filtering. J Math Imaging Vis 60, 1399–1426 (2018). https://doi.org/10.1007/s10851-018-0847-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10851-018-0847-4