Publications

Journals, Conferences etc

[1] Y. Korolev and J. Lellmann, “Image reconstruction with imperfect forward models and applications in deblurring,” SIAM Journal on Imaging Sciences, vol. 11, no. 1, pp. 197--218, 2018. [ bib | DOI | arXiv | http ]
[2] 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 ]
[3] P. J. Markiewicz, M. J. Ehrhardt, K. Erlandsson, P. J. Noonan, A. Barnes, J. M. Schott, D. Atkinson, S. R. Arridge, B. F. Hutton, and S. Ourselin, “NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis,” Neuroinformatics, vol. 16, no. 1, pp. 95--115, 2018. [ bib | DOI ]
[4] Y. J. Tsai, A. Bousse, M. J. Ehrhardt, C. W. Stearns, S. Ahn, B. F. Hutton, S. Arridge, and K. Thielemans, “Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning,” IEEE Transactions on Medical Imaging, vol. 37, no. 4, pp. 1000--1010, 2018. [ bib | DOI ]
[5] M. J. Ehrhardt, E. S. Riis, T. Ringholm, and C.-B. Schönlieb, “A Geometric Integration Approach to Smooth Optimisation: Foundations of the Discrete Gradient Method.” 2018. [ bib | arXiv | http ]
[6] 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,” Inverse Problems, vol. 34, no. 4, p. 044003, 2018. [ bib | arXiv | http ]
[7] 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 ]
[8] 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 ]
[9] 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 ]
[10] 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 ]
[11] M. Benning, M. M. Betcke, M. J. Ehrhardt, and C.-B. Schönlieb, “Choose your path wisely: Gradient Descent in a Bregman Distance Framework,” submitted to SIAM Journal on Optimization, 2017. [ bib ]
[12] 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 ]
[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] A. Gorokh, Y. Korolev, and T. Valkonen, “Diffusion tensor imaging with deterministic error bounds,” Journal of Mathematical Imaging and Vision, vol. 56, no. 1, pp. 137--157, 2016. [ bib | DOI | arXiv | http ]
[15] 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 ]
[16] 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 ]
[17] 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 ]
[18] 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 ]
[19] 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 ]
[20] 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 ]
[21] 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 ]
[22] 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 ]
[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] 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 ]
[25] 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 ]
[26] M. Hintermüller, T. Valkonen, and T. Wu, “Limiting aspects of non-convex TVφ models,” SIAM Journal on Imaging Sciences, 2015. Accepted. [ bib | arXiv | .pdf ]
[27] 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 ]
[28] 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 ]
[29] 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 ]
[30] 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 ]
[31] 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 ]
[32] 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 ]
[33] 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 ]
[34] J. Lellmann, D. A. Lorenz, C. Schönlieb, and T. Valkonen, “Imaging with kantorovich-rubinstein discrepancy,” preprint, arXiV 1407.0221 [cs.CV], July 2014. [ bib ]
[35] Y. Korolev, “Making use of a partial order in solving inverse problems: II,” Inverse Problems, vol. 30, no. 8, p. 085003, 2014. [ bib | DOI | http ]
[36] 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 ]
[37] 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 ]
[38] 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 ]
[39] 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 ]
[40] 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 ]
[41] 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 ]
[42] 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 ]
[43] 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 ]
[44] 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 ]
[45] Y. Korolev and A. Yagola, “Making use of a partial order in solving inverse problems,” Inverse Problems, vol. 29, no. 9, p. 095012, 2013. [ bib | DOI ]
[46] K. Papafitsoros and K. Bredies, “A study of the one dimensional total generalised variation regularisation problem.” Preprint, 2013. [ bib | .pdf ]
[47] T. Valkonen, “A primal-dual hybrid gradient method for non-linear operators wit applications to MRI.” Submitted, 2013. [ bib | .pdf ]
[48] 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 ]
[49] T. Valkonen, K. Bredies, and F. Knoll, “Total generalised variation in diffusion tensor imaging,” SIAM Journal on Imaging Sciences, 2013. Accepted. [ bib | .pdf ]
[50] 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 ]
[51] 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 ]
[52] 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 ]
[53] 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 ]
[54] 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 ]
[55] 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 ]
[56] 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 ]
[57] 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 ]
[58] J. Lellmann and C. Schönlieb, “Workshop on statistics, learning and variational methods in imaging at the University of Cambridge,” September 2012. [ bib ]
[59] 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 ]
[60] 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 ]
[61] 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 ]
[62] C.-B. Schönlieb, “Applying modern pde techniques to digital image restoration,” Matlab Digest Newsletters, pp. 1--5, 2012. [ bib | .html ]
[63] 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 ]
[64] 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 ]
[65] 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 ]
[66] 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 ]
[67] 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 ]
[68] Y. Korolev and A. Yagola, “On inverse problems in partially ordered spaces with a priori information,” Journal of Inverse and Ill-posed problems, vol. 20, no. 4, 2012. [ bib | DOI ]
[69] T. Valkonen, “A method for weighted projections to the positive definite cone,” SFB-Report 2012-016, Karl-Franzens University of Graz, 2012. [ bib | .pdf ]
[70] T. Valkonen, “Strong polyhedral approximation of simple jump sets,” Nonlinear Analysis: Theory, Methods, & Applications, vol. 75, pp. 3641--3671, 2012. [ bib | DOI | .pdf ]
[71] 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 ]
[72] 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 ]
[73] 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 ]
[74] 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 ]
[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] 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 ]
[77] 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 ]
[78] T. Valkonen, “A primal-dual interior point method for diff-convex problems on symmetric cones,” Optimization, 2011. Published online. [ bib | DOI | .pdf ]
[79] 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 ]
[80] J. Lellmann and C. Schnörr, “Continuous multiclass labeling approaches and algorithms,” J. Imaging Sci., vol. 4, no. 4, pp. 1049--1096, 2011. [ bib ]
[81] 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 ]
[82] 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 ]
[83] J. Lellmann, “Nonsmooth convex variational approaches to image analysis,” 2011. [ bib ]
[84] D. Breitenreicher, J. Lellmann, and C. Schnörr, “Sparse template-based variational image segmentation,” Adv. Adaptive Data Analysis, vol. 3, pp. 149--166, 2011. [ bib ]
[85] 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 ]
[86] J. Lellmann and C. Schnörr, “Continuous multiclass labeling approaches and algorithms,” tech. rep., Univ. of Heidelberg, February 2010. [ bib ]
[87] 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 ]
[88] 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 ]
[89] J. D. Rossi and C.-B. Schönlieb, “Nonlocal higher order evolution equations,” Applicable Analysis, vol. 89, no. 6, pp. 949--960, 2010. [ bib | .pdf ]
[90] 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 ]
[91] 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 ]
[92] 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 ]
[93] C.-B. Schönlieb, Modern PDE Techniques for Image Inpainting. PhD thesis, University of Cambridge, 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] 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 ]
[96] 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 ]
[97] W. Baatz, M. Fornasier, P. Markowich, and C.-B. Schönlieb, “Binary based fresco restoration,” in Proceedings of Bridges, pp. 337--338, 2009. [ bib | .pdf ]
[98] 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 ]
[99] 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 ]
[100] T. Valkonen, “Optimal transportation networks and stations,” Interfaces and Free Boundaries, vol. 11, no. 4, pp. 569--597, 2009. [ bib | DOI | .pdf ]
[101] 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 ]
[102] 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 ]
[103] 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 ]
[104] 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 ]
[105] C.-B. Schönlieb, “How differential equations can make a bungee jumper jump without a rope,” tech. rep., 2008. [ bib | .pdf ]
[106] 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 ]
[107] 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 ]
[108] 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 ]
[109] 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 ]
[110] T. Valkonen, “Diff-convex combinations of Euclidean distances: a search for optima,” 2008. Ph.D Thesis. [ bib | .pdf ]
[111] 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 ]
[112] 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 ]
[113] 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 ]
[114] 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 ]
[115] 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 ]
[116] 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 ]
[117] 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 ]

This file was generated by bibtex2html 1.99.

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] 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 ]
[3] L. Calatroni, New PDE models for imaging problems and applications. PhD thesis, University of Cambridge, 2016. [ bib | http ]
[4] J. Lee, Mapping individual trees from airborne multi-sensor imagery. 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 1.99.




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.