Journals, Conferences etc

[1] M. Burger, B. Düring, L. M. Kreusser, P. A. Markowich, and C.-B. Schönlieb, “Pattern formation of a nonlocal, anisotropic interaction model,” (to appear in) Mathematical Models and Methods in Applied Sciences, 2018. [ bib | arXiv | http ]
[2] A. Chambolle, M. J. Ehrhardt, P. Richtárik, and C.-B. Schönlieb, “Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications,” tech. rep., June 2017. [ bib | arXiv | http ]
[3] M. J. Ehrhardt, P. J. Markiewicz, P. Richtárik, J. Schott, A. Chambolle, and C.-B. Schönlieb, “Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method,” in SPIE Optics+Photonics:Wavelets and Sparsity XVII, (San Diego), 2017. [ bib | DOI | http ]
[4] L. Bungert, D. A. Coomes, M. J. Ehrhardt, J. Rasch, R. Reisenhofer, and C.-B. Schönlieb, “Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation.” 2017. [ bib | arXiv | http ]
[5] A. Klar, L. M. Kreusser, and O. Tse, “Trend to equilibrium for a delay Vlasov-Fokker-Planck equation and explicit decay estimates,” (to appear in) SIAM Journal on Mathematical Analysis, 2017. [ bib | arXiv | http ]
[6] J. S. Grah, J. A. Harrington, S. B. Koh, J. A. Pike, A. Schreiner, M. Burger, C.-B. Schönlieb, and S. Reichelt, “Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy,” Methods, vol. 115, pp. 91 -- 99, 2017. Image Processing for Biologists. [ bib | DOI | http ]
[7] M. Benning, G. Gilboa, J. S. Grah, and C.-B. Schönlieb, “Learning filter functions in regularisers by minimising quotients,” in International Conference on Scale Space and Variational Methods in Computer Vision, pp. 511--523, Springer, 2017. [ bib | arXiv | http ]
[8] M. J. Ehrhardt, P. Markiewicz, M. Liljeroth, A. Barnes, V. Kolehmainen, J. Duncan, L. Pizarro, D. Atkinson, B. F. Hutton, S. Ourselin, K. Thielemans, and S. R. Arridge, “PET Reconstruction with an Anatomical MRI Prior using Parallel Level Sets,” IEEE Transactions on Medical Imaging, vol. 35, no. 9, pp. 2189--2199, 2016. [ bib | DOI ]
[9] M. J. Ehrhardt and M. M. Betcke, “Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation,” SIAM Journal on Imaging Sciences, vol. 9, no. 3, pp. 1084--1106, 2016. [ bib | DOI | arXiv ]
[10] M. Benning, M. M. Betcke, M. J. Ehrhardt, and C.-B. Schönlieb, “Gradient Descent in a Generalised Bregman Distance Framework,” in Geometric Numerical Integration and its Applications, pp. 40----45, MI Lecture Notes series of Kyushu University, 2016. [ bib | arXiv | http ]
[11] M. Benning, F. Knoll, C.-B. Schönlieb, and T. Valkonen, “Preconditioned admm with nonlinear operator constraint,” (to appear in) IFIP Advances in Information and Communication Technology, 2016. [ bib ]
[12] N. Ramskill, I. Bush, A. Sederman, M. Mantle, M. Benning, B. Anger, M. Appel, and L. Gladden, “Fast imaging of laboratory core floods using 3d compressed sensing rare mri,” Journal of Magnetic Resonance, 2016. [ bib ]
[13] J. Lee, X. Cai, J. Lellmann, M. Dalponte, Y. Malhi, N. Butt, M. Morecroft, C.-B. Schönlieb, and D. A. Coomes, “Individual tree species classification from airborne multisensor imagery using robust pca,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 6, pp. 2554--2567, 2016. [ bib ]
[14] K. Papafitsoros and T. Valkonen, “Asymptotic behaviour of total generalised variation,” in Scale Space and Variational Methods in Computer Vision (SSVM 2015), vol. 9087 of Lecture Notes in Computer Science, pp. 702--714, 2015. [ bib | DOI | arXiv | .pdf ]
[15] L. Calatroni, C. Cao, J. C. de Los Reyes, C.-B. Schönlieb, and T. Valkonen, “Bilevel approaches for learning of variational imaging models.” Submitted, 2015. [ bib | arXiv | .pdf ]
[16] J. C. de Los Reyes, C.-B. Schönlieb, and T. Valkonen, “The structure of optimal parameters for image restoration problems.” Submitted, 2015. [ bib | arXiv | .pdf ]
[17] J. C. de Los Reyes, C.-B. Schönlieb, and T. Valkonen, “Bilevel parameter learning for higher-order total variation regularisation models.” submitted, 2015. [ bib | arXiv | .pdf ]
[18] A. Gorokh, Y. Korolev, and T. Valkonen, “Diffusion tensor imaging with deterministic error bounds.” submitted, 2015. [ bib | .pdf ]
[19] T. Valkonen, “Optimising big images,” in Big Data Optimization: Recent Developments and Challenges (A. Emrouznejad, ed.), Studies in Big Data, Springer-Verlag, 2015. Accepted. [ bib ]
[20] M. Hintermüller, T. Valkonen, and T. Wu, “Limiting aspects of non-convex TVφ models,” SIAM Journal on Imaging Sciences, 2015. Accepted. [ bib | arXiv | .pdf ]
[21] M. Moeller, M. Benning, C. Schönlieb, and D. Cremers, “Variational depth from focus reconstruction,” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5369--5378, 2015. [ bib ]
[22] Z. Saghi, M. Benning, R. Leary, M. Macias-Montero, A. Borras, and P. A. Midgley, “Reduced-dose and high-speed acquisition strategies for multi-dimensional electron microscopy,” Advanced Structural and Chemical Imaging, vol. 1, no. 1, pp. 1--10, 2015. [ bib ]
[23] E. von Harbou, H. T. Fabich, M. Benning, A. B. Tayler, A. J. Sederman, L. F. Gladden, and D. J. Holland, “Quantitative mapping of chemical compositions with mri using compressed sensing,” Journal of Magnetic Resonance, vol. 261, pp. 27--37, 2015. [ bib ]
[24] E.-M. Brinkmann, M. Burger, and J. Grah, “Regularization with sparse vector fields: from image compression to tv-type reconstruction,” in International Conference on Scale Space and Variational Methods in Computer Vision, pp. 191--202, Springer, 2015. [ bib | arXiv | http ]
[25] J. Lee, X. Cai, C.-B. Schönlieb, and D. A. Coomes, “Nonparametric image registration of airborne lidar, hyperspectral and photographic imagery of wooded landscapes,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 11, pp. 6073--6084, 2015. [ bib ]
[26] J. Lee, X. Cai, C.-B. Schönlieb, and D. Coomes, “Mapping individual trees from airborne multi-sensor imagery,” in Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International, pp. 5411--5414, IEEE, 2015. [ bib ]
[27] J. Lellmann, D. A. Lorenz, C. Schönlieb, and T. Valkonen, “Imaging with kantorovich-rubinstein discrepancy,” preprint, arXiV 1407.0221 [cs.CV], July 2014. [ bib ]
[28] T. Valkonen, “The jump set under geometric regularisation. Part 1: Basic technique and first-order denoising,” tech. rep., arXiv: 1407.1531 [math.FA], July 2014. Submitted. [ bib | arXiv | .pdf ]
[29] T. Valkonen, “The jump set under geometric regularisation. Part 2: Higher-order approaches,” tech. rep., arXiv: 1407.2334 [math.FA], July 2014. Submitted. [ bib | arXiv | .pdf ]
[30] J. Lellmann, K. Papafitsoros, C. Schönlieb, and D. Spector, “Analysis and application of a non-local hessian,” preprint, arXiv 1410.8825 [math.NA], 2014. [ bib ]
[31] F. Lenzen, J. Lellmann, F. Becker, and C. Schnörr, “Solving qvis for image denoising with adaptive constraint sets,” J. Imaging Sci., p. to appear, 2014. [ bib ]
[32] A. B. Tayler, M. Benning, A. J. Sederman, D. J. Holland, and L. F. Gladden, “Ultrafast magnetic-resonance-imaging velocimetry of liquid-liquid systems: Overcoming chemical-shift artifacts using compressed sensing,” Physical Review E, vol. 89, no. 6, p. 063009, 2014. [ bib ]
[33] H. T. Fabich, M. Benning, A. J. Sederman, and D. J. Holland, “Ultrashort echo time (ute) imaging using gradient pre-equalization and compressed sensing,” Journal of Magnetic Resonance, vol. 245, pp. 116--124, 2014. [ bib ]
[34] E. Bae, J. Lellmann, and D. Cremers, “Convex relaxations for a generalized chan-vese model,” in Energy Min. Meth. Comp. Vis. Pattern Recogn., 2013. [ bib ]
[35] J. H. Kappes, B. Andres, F. A. Hamprecht, C. Schnörr, S. Nowozin, D. Batra, S. Kim, B. X. Kausler, J. Lellmann, N. Komodakis, and C. Rother, “A comparative study of modern inference techniques for discrete energy minimization problems,” in Comp. Vis. Pattern Recogn., 2013. [ bib ]
[36] J. Lellmann, B. Lellmann, F. Widmann, and C. Schnörr, “Discrete and continuous models for partitioning problems,” Int. J. Comp. Vis, vol. 104, no. 3, pp. 241--269, 2013. [ bib ]
[37] J. Lellmann, F. Lenzen, and C. Schnörr, “Optimality bounds for a variational relaxation of the image partitioning problem,” J. Math. Imaging Vis., vol. 47, no. 3, pp. 239--257, 2013. [ bib ]
[38] J. Lellmann, J.-M. Morel, and C. Schönlieb, “Anisotropic third-order regularization for sparse digital elevation models,” in Scale Space Var. Meth., vol. 7893 of Springer LNCS, pp. 161--173, 2013. [ bib ]
[39] J. Lellmann, E. Strekalovskiy, S. Koetter, and D. Cremers, “Total variation regularization for functions with values in a manifold,” in Int. Conf. Comp. Vis., 2013. [ bib ]
[40] F. Lenzen, F. Becker, and J. Lellmann, “Adaptive second-order total variation: An approach aware of surface discontinuities,” in Scale Space Var. Meth., 2013. [ bib ]
[41] K. Bredies, K. Kunisch, and T. Valkonen, “Properties of L1-TGV2: The one-dimensional case,” Journal of Mathematical Analysis and Applications, vol. 398, pp. 438--454, 2013. [ bib | DOI | .pdf ]
[42] T. Valkonen, K. Bredies, and F. Knoll, “Total generalised variation in diffusion tensor imaging,” SIAM Journal on Imaging Sciences, 2013. Accepted. [ bib | .pdf ]
[43] M. Benning, L. Gladden, D. Holland, C.-B. Schönlieb, and T. Valkonen, “Phase reconstruction from velocity-encoded MRI measurements -- A survey of sparsity-promoting variational approaches.” Submitted, 2013. [ bib | .pdf ]
[44] T. Valkonen, “A primal-dual hybrid gradient method for non-linear operators wit applications to MRI.” Submitted, 2013. [ bib | .pdf ]
[45] K. Papafitsoros and K. Bredies, “A study of the one dimensional total generalised variation regularisation problem.” Preprint, 2013. [ bib | .pdf ]
[46] M. Benning, L. Calatroni, B. Düring, and C.-B. Schönlieb, “A primal-dual approach for a total variation wasserstein flow,” in Geometric Science of Information, pp. 1--8, 2013. [ bib | .pdf ]
[47] L. Calatroni, B. Düring, and C.-B. Schönlieb, “Adi splitting schemes for a fourth-order nonlinear partial differential equation from image processing,” Discrete & Continuous Dynamical Systems -- Series A, pp. 1--27, 2013. [ bib | .pdf ]
[48] K. Papafitsoros, C.-B. Schönlieb, and B. Sengul, “Combined first and second order total variation inpainting using split bregman,” Image Processing Online, pp. 1--25, 2013. [ bib | .pdf ]
[49] F. Schubert and C.-B. Schönlieb, “Random simulations for generative art construction -- some examples,” Journal of Mathematics and the Arts, pp. 1--14, 2013. [ bib | .pdf ]
[50] A. Langer, S. Osher, and C.-B. Schönlieb, “Bregmanized domain decomposition for image restoration,” Journal of Scientific Computing, vol. 54, no. 2--3, pp. 549--576, 2013. [ bib | .pdf ]
[51] J. Lellmann and C. Schönlieb, “Workshop on statistics, learning and variational methods in imaging at the University of Cambridge,” September 2012. [ bib ]
[52] C. Gottschlich and C.-B. Schönlieb, “Oriented diffusion filtering for enhancing low-quality fingerprint images,” IET Biometrics, vol. 1, pp. 105--113, June 2012. [ bib | .pdf ]
[53] T. Valkonen, F. Knoll, and K. Bredies, “TGV for diffusion tensors: A comparison of fidelity functions,” in Journal of Inverse and Ill-posed problems special issue for IP:M&S 2012, (Antalya, Turkey), May 2012. To appear. [ bib | .pdf ]
[54] T. Valkonen and M. Liebmann, “GPU-accelerated regularisation of large diffusion-tensor volumes,” in Computing special issue for ESCO 2012, (Pilsen, Czech Republic), May 2012. To appear. [ bib | DOI | .pdf ]
[55] D. Breitenreicher, J. Lellmann, and C. Schnörr, “COAL: A generic modelling and prototyping framework for convex optimization problems of variational image analysis,” Optim. Meth. Softw., pp. 1--14, 2012. [ bib ]
[56] F. Lenzen, F. Becker, J. Lellmann, S. Petra, and C. Schnörr, “A class of quasi-variational inequalities for adaptive image denoising and decomposition,” Comput. Optim. Appl., vol. 54, no. 2, pp. 371--398, 2012. [ bib ]
[57] F. Lenzen, F. Becker, J. Lellmann, S. Petra, and C. Schnörr, “Variational image denoising with adaptive constraint sets,” in Scale Space Var. Meth., vol. 6667 of Springer LNCS, pp. 206--217, 2012. [ bib ]
[58] T. Valkonen, “Strong polyhedral approximation of simple jump sets,” Nonlinear Analysis: Theory, Methods, & Applications, vol. 75, pp. 3641--3671, 2012. [ bib | DOI | .pdf ]
[59] T. Valkonen, “A method for weighted projections to the positive definite cone,” SFB-Report 2012-016, Karl-Franzens University of Graz, 2012. [ bib | .pdf ]
[60] J.-C. De Los Reyes and C.-B. Schönlieb, “Image denoising: Learning noise distribution via pde-constrained optimisation,” Inverse Problems and Imaging, pp. 1--23, 2012. [ bib | .pdf ]
[61] K. Papafitsoros and C.-B. Schönlieb, “A combined first and second order variational approach for image reconstruction,” Journal of Mathematical Imaging and Vision, pp. 1--33, 2012. [ bib | http ]
[62] M. Fornasier, Y. Kim, A. Langer, S. Osher, and C.-B. Schönlieb, “Wavelet decomposition method for l2/tv-image deblurring,” SIAM J. Imaging Sci., vol. 5, no. 3, pp. 857--885, 2012. [ bib | .pdf ]
[63] M. Burger, M. Franek, and C.-B. Schönlieb, “Regularised regression and density estimation based on optimal transport,” Appl. Math. Res. Express, vol. 2, pp. 209--253, 2012. [ bib | .pdf ]
[64] B. Düring and C.-B. Schönlieb, “A high-contrast fourth-order pde from imaging: numerical solution by adi splitting,” in AMS Contemporary Mathematics, pp. 1--11, 2012. [ bib | .pdf ]
[65] C.-B. Schönlieb, “Applying modern pde techniques to digital image restoration,” Matlab Digest Newsletters, pp. 1--5, 2012. [ bib | .html ]
[66] D. Breitenreicher, J. Lellmann, and C. Schnörr, “Sparse template-based variational image segmentation,” Adv. Adaptive Data Analysis, vol. 3, pp. 149--166, 2011. [ bib ]
[67] J. Lellmann, “Nonsmooth convex variational approaches to image analysis,” 2011. [ bib ]
[68] J. Lellmann, F. Lenzen, and C. Schnörr, “Optimality bounds for a variational relaxation of the image partitioning problem,” in Energy Min. Meth. Comp. Vis. Patt. Recogn., vol. 6819 of Springer LNCS, pp. 132--146, 2011. [ bib ]
[69] J. Lellmann, F. Lenzen, and C. Schnörr, “Optimality bounds for a variational relaxation of the image partitioning problem,” Tech. Rep. arXiv:1112.0974v1 [cs.CV], 2011. [ bib ]
[70] J. Lellmann and C. Schnörr, “Continuous multiclass labeling approaches and algorithms,” J. Imaging Sci., vol. 4, no. 4, pp. 1049--1096, 2011. [ bib ]
[71] J. Lellmann and C. Schnörr, “Regularizers for vector-valued data and labeling problems in image processing,” Controls Systems and Computers, vol. 2, pp. 43--54, 2011. [ bib ]
[72] T. Valkonen, “A primal-dual interior point method for diff-convex problems on symmetric cones,” Optimization, 2011. Published online. [ bib | DOI | .pdf ]
[73] T. Valkonen, “Transport equation and image interpolation with SBD velocity fields,” Journal de mathématiques pures et appliquées, vol. 95, pp. 459--494, 2011. [ bib | DOI | .pdf ]
[74] K. Bredies and T. Valkonen, “Inverse problems with second-order total generalized variation constraints,” in Proceedings of SampTA 2011 -- 9th International Conference on Sampling Theory and Applications, Singapore, 2011. [ bib | .pdf ]
[75] C.-B. Schönlieb and A. Bertozzi, “Unconditionally stable schemes for higher order inpainting,” Communications in Mathematical Sciences, vol. 9, no. 2, pp. 413--457, 2011. [ bib | .pdf ]
[76] P.-E. Barbano, A. Fokas, and C.-B. Schönlieb, “Alternating regularisation in measurement- and image space for pet reconstruction,” in Proc. Int. Conf. Sampling Theory and Applications, pp. 1--4, 2011. [ bib | .pdf ]
[77] T. Valkonen and T. Kärkkäinen, “Clustering and the perturbed spatial median,” Mathematical and Computer Modelling, vol. 52, pp. 87--106, July 2010. [ bib | DOI | .pdf ]
[78] J. Lellmann and C. Schnörr, “Continuous multiclass labeling approaches and algorithms,” tech. rep., Univ. of Heidelberg, February 2010. [ bib ]
[79] J. Lellmann, D. Breitenreicher, and C. Schnörr, “Fast and exact primal-dual iterations for variational problems in computer vision,” in Europ. Conf. Comp. Vis., vol. 6312 of Springer LNCS, pp. 494--505, 2010. [ bib ]
[80] T. Valkonen, “Refined optimality conditions for differences of convex functions,” Journal of Global Optimization, vol. 48, no. 2, pp. 311--321, 2010. [ bib | DOI | .pdf ]
[81] M. Fornasier, A. Langer, and C.-B. Schönlieb, “A convergent overlapping domain decomposition method for total variation minimization,” Numerische Mathematik, vol. 116, no. 4, pp. 645--685, 2010. [ bib | .pdf ]
[82] J. D. Rossi and C.-B. Schönlieb, “Nonlocal higher order evolution equations,” Applicable Analysis, vol. 89, no. 6, pp. 949--960, 2010. [ bib | .pdf ]
[83] U. Bauer, C.-B. Schönlieb, and M. Wardetzky, “Total variation meets topological persistence: A first encounter,” in AIP Proceedings, ICNAAM, Numerical Analysis and Applied Mathematics, pp. 1022--1025, 2010. [ bib | .pdf ]
[84] M. Kostner, F. Schubert, and C.-B. Schönlieb, “Chaos, noise, randomness and coincidence as constitutional for generative art,” in Proceedings of Bridges, pp. 467--470, 2010. [ bib | .pdf ]
[85] J. Lellmann, F. Becker, and C. Schnörr, “Convex optimization for multi-class image labeling with a novel family of total variation based regularizers,” in Int. Conf. Comp. Vis., pp. 646--653, 2009. [ bib ]
[86] J. Lellmann, J. Kappes, J. Yuan, F. Becker, and C. Schnörr, “Convex multi-class image labeling by simplex-constrained total variation,” in Scale Space Var. Meth., vol. 5567 of Springer LNCS, pp. 150--162, 2009. [ bib ]
[87] T. Valkonen and T. Kärkkäinen, “Continuous reformulations and heuristics for the Euclidean travelling salesperson problem,” ESAIM: Control, Optimization and Calculus of Variations, vol. 15, no. 4, 2009. [ bib ]
[88] T. Valkonen, “Optimal transportation networks and stations,” Interfaces and Free Boundaries, vol. 11, no. 4, pp. 569--597, 2009. [ bib | DOI | .pdf ]
[89] M. Burger, L. He, and C.-B. Schönlieb, “Cahn-hilliard inpainting and a generalization for grayvalue images,” SIAM J. Imaging Sci., vol. 2, no. 4, pp. 1129--1167, 2009. [ bib | .pdf ]
[90] M. Fornasier and C.-B. Schönlieb, “Subspace correction methods for total variation and l1-minimization,” SIAM J. Numer. Anal., vol. 47, no. 5, pp. 3397--3428, 2009. [ bib | .pdf ]
[91] W. Baatz, M. Fornasier, P. Markowich, and C.-B. Schönlieb, “Binary based fresco restoration,” in Proceedings of Bridges, pp. 337--338, 2009. [ bib | .pdf ]
[92] C.-B. Schönlieb, A. Bertozzi, M. Burger, and L. He, “Image inpainting using a fourth-order total variation flow,” in Proc. Int. Conf. Sampling Theory and Applications, pp. 1--4, 2009. [ bib | .pdf ]
[93] A. Langer, M. Fornasier, and C.-B. Schönlieb, “Domain decomposition methods for compressed sensing,” in Proc. Int. Conf. Sampling Theory and Applications, pp. 1--4, 2009. [ bib | .pdf ]
[94] C.-B. Schönlieb, “Total variation minimization with an H-1 constraint,” in Singularities in Nonlinear Evolution Phenomena and Applications, pp. 201--232, 2009. [ bib | .pdf ]
[95] C.-B. Schönlieb, Modern PDE Techniques for Image Inpainting. PhD thesis, University of Cambridge, 2009. [ bib | .pdf ]
[96] R. Glowinski, T. Kärkkäinen, T. Valkonen, and A. Ivannikov, “Nonsmooth SOR for L1-fitting: Convergence study and discussion of related issues,” Journal of Scientific Computing, vol. 37, pp. 103--138, Nov. 2008. [ bib | DOI | .pdf ]
[97] J. Lellmann, J. Balzer, A. Rieder, and J. Beyerer, “Shape from specular reflection and optical flow,” Int. J. Comp. Vis., vol. 80, no. 2, pp. 226--241, 2008. [ bib ]
[98] J. Lellmann, J. Kappes, J. Yuan, F. Becker, and C. Schnörr, “Convex multi-class image labeling by simplex-constrained total variation,” tech. rep., Univ. of Heidelberg, 2008. [ bib ]
[99] T. Valkonen, “Diff-convex combinations of Euclidean distances: a search for optima,” 2008. Ph.D Thesis. [ bib | .pdf ]
[100] J. F. Bonder, J. D. Rossi, and C.-B. Schönlieb, “The best constant and extremals of the sobolev embeddings in domains with holes: the l-infinity case,” Illinois Journal of Mathematics, vol. 52, no. 4, pp. 1111--1121, 2008. [ bib | .pdf ]
[101] J. F. Bonder, J. D. Rossi, and C.-B. Schönlieb, “An optimization problem related to the best sobolev trace constant in thin domains,” Communications in Contemporary Mathematics, vol. 10, no. 5, pp. 633--650, 2008. [ bib | .pdf ]
[102] M. Burger, S.-Y. Chu, P. Markowich, and C.-B. Schönlieb, “The willmore functional and instabilities in the cahn-hilliard equation,” Communications in Mathematical Sciences, vol. 6, no. 2, pp. 309--329, 2008. [ bib | .pdf ]
[103] W. Baatz, M. Fornasier, P. Markowich, and C.-B. Schönlieb, “Inpainting of ancient austrian frescoes,” in Proceedings of Bridges, pp. 150--156, 2008. [ bib | .pdf ]
[104] C.-B. Schönlieb, “How differential equations can make a bungee jumper jump without a rope,” tech. rep., 2008. [ bib | .pdf ]
[105] T. Valkonen, “Convergence of a SOR-Weiszfeld type algorithm for incomplete data sets,” Numerical Functional Analysis and Optimization, vol. 27, pp. 931--952, Dec. 2006. (Errata in vol. 29, no 9--10, 2008). [ bib | DOI ]
[106] J. Albiez, B. Giesler, J. Lellmann, J. M. Zöllner, and R. Dillmann, “Virtual immersion for tele-controlling a hexapod robot,” in Climbing and Walking Robots, pp. 1087--1094, 2006. [ bib | DOI ]
[107] C.-B. Schönlieb and K. C. Wang, “Line segmentation and analysis with special interest to the duct of a line,” in Proceedings Fourth Indian Conference on Computer Vision, Graphics and Image Processing, pp. 550--555, 2004. [ bib ]
[108] C.-B. Schönlieb and K. C. Wang, “Feature selection and clustering in arts analysis,” in Proceedings Irish Machine Vision and Image Processing Conference, pp. 25--31, 2003. [ bib ]
[109] P. Gerl, C.-B. Schönlieb, and K. C. Wang, “Automatisierte analyse und klassifikation von zeichnungen und gemälden,” in Konferenzband Electronic Imaging and the Visual Arts, pp. 123--126, 2003. [ bib ]

This file was generated by bibtex2html 1.98.

CIA Dissertations

[1] J. S. Grah, Mathematical Imaging Tools in Cancer Research - From Mitosis Analysis to Sparse Regularisation. PhD thesis, University of Cambridge, 2017. [ bib | http ]
[2] J. Lee, Mapping individual trees from airborne multi-sensor imagery. PhD thesis, University of Cambridge, 2016. [ bib | http ]
[3] L. Calatroni, New PDE models for imaging problems and applications. PhD thesis, University of Cambridge, 2016. [ bib | http ]
[4] E. Papoutsellis, First-order gradient regularisation methods for image restoration: reconstruction of tomographic images with thin structures and denoising piecewise affine images. PhD thesis, University of Cambridge, 2016. [ bib | http ]
[5] K. Papafitsoros, Novel higher order regularisation methods for image reconstruction. PhD thesis, University of Cambridge, 2015. [ bib | http ]

This file was generated by bibtex2html 1.98.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All person copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.