[17] N. Huitzil Santamaría, T. Batard and C. Brito-Loeza: Nonlinear DIP-DiracVTV Model for Color Image Restoration. Proceedings of the Mexican International Conference on Artificial Intelligence, pp 168-181. [pdf]
[16] K. Chin and T. Batard: Nonlinear L2-DiracVTV Model for Color Image Restoration. Proceedings of the Mexican International Conference on Artificial Intelligence, pp 198-212. [pdf]
[15] E. Zúniga, T. Batard and J.-B. Hayet: T(G)V NeRF: A strong Baseline in Regularized Neural Radiance Fields with few training Views. Proceedings of the Mexican International Conference on Artificial Intelligence, pp 152-167. [pdf]
[13] T. Batard, E. Ramon Maldonado, G. Steidl and M. Bertalmío: A Connection between Image Processing and Artificial Neural Networks Layers through a Geometric Model of Visual Perception. 7th Int. Conf. on Scale Space and Variational Methods in Computer Vision SSVM 2019, LNCS 11603 (J.Lellmann et al. Eds.), Springer, 2019, pp. 459-471. [pdf]
[12] G. Ghimpeteanu, D. Kane, T. Batard, S. Levine and M. Bertalmío: Local Denoising Based on Curvature Smoothing can Visually Outperform Non-local Methods on Photographs with Actual Noise. Proceedings of the IEEE Int. Conf. on Image Processing ICIP 2016. [pdf]
[11] G. Ghimpeteanu, T. Batard, T. Seybold and M. Bertalmío: Local Denoising applied to RAW Images may outperform non-local patch-based Methods applied to the Camera Output. IS&T Electronic Imaging Conference 2016. [pdf]
[10] T. Batard and M. Bertalmío: Duality Principle for Image Regularization and Perceptual Color Correction Models. 5th Int. Conf.on Scale Space and Variational Methods in Computer Vision SSVM 2015, LNCS 9087 (J.-F. Aujol et al. Eds.), Springer, 2015, pp. 449-460. [pdf]
[9] G. Ghimpeteanu, T. Batard, M. Bertalmío, and S. Levine: Denoising an Image by Denoising its Components in a Moving Frame. Proceedings of the 6th Int. Conf. on Image and Signal Processing ICISP 2014, LNCS 8509 (A. Elmoataz Eds.), Springer 2014, pp. 375-383. [pdf] Best Paper Award.
[8] T. Batard and M. Bertalmío: Harmonic Flow for Histogram Matching. Image and Video Technology- PSIVT 2013 Workshops, LNCS 8334 (F. Huang and A. Sugimoto Eds.), Springer, 2014, pp. 145-156. [pdf]
[7] P. Cyriac, T. Batard, and M. Bertalmío: A Variational Method for the Optimization of Tone-Mapping Operators. Proceedings of the 6th Pacific-Rim Symposium on Image and Video Technology PSIVT 2013, LNCS 8333 (R. Klette et al. Eds.), Springer, 2014, pp. 505-516. [pdf]
[6] T. Batard and M. Berthier: A Riemannian Fourier Transform via Spin Representations. 1st Int. Conf. Geometric Science of Information GSI 2013, LNCS 8085 (F. Nielsen and F. Barbaresco Eds.), Springer, 2013, pp. 131-139. [pdf]
[5] T. Batard and M. Bertalmío: Generalized Gradient on Vector Bundle - Application to Image Denoising. 4th Int. Conf. on Scale Space and Variational Methods in Computer Vision SSVM 2013, LNCS 7893 (A. Kuijper et al. Eds.), Springer, 2013, pp. 12-23. [pdf]
[4] T. Batard and N. Sochen: Polyakov Action on (\rho,G)-equivariant Functions - Application to Color Image Regularization. 3rd Int. Conf. on Scale Space and Variationnal Methods in Computer Vision SSVM 2011, LNCS 6667 (A.M. Bruckstein et al. Eds), Springer, 2012, pp. 483-494. [pdf]
[3] T. Batard and M. Berthier: Heat Kernels of Generalized Laplacians - Application to Color Image Smoothing. IEEE Int. Conf. on Image Processing ICIP 2009. [pdf]
[2] T. Batard and M. Berthier: The Clifford-Hodge Flow: an Extension of the Beltrami Flow. Computer Analysis of Images and Patterns, 13th Int. Conf. CAIP 2009 (X. Jiang and N.Petkov Eds.), LNCS 5702,Springer, 2009, pp. 394-401. [pdf]
[1] H. Le Capitaine, T. Batard, C. Frélicot, and M. Berthier: Blockwise Similarity in [0,1] via Triangular Norms and Sugeno Integrals - Application to Cluster Validity. IEEE Int. Conf. on Fuzzy Systems FUZZIEEE 2007. [pdf]