Publications

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Bertalmío M, Levine S.  2012.  "Denoising an image by denoising its curvature image", IMA Preprint.
Bertalmío M.  2014.  A model of color constancy and efficient coding can predict lightness induction. Vision Sciences Society Annual Meeting. vss2014.pdf (2.95 MB)
Bertalmío M, Calatroni L, Franceschi V, Franceschiello B, Prandi D.  2020.  Cortical-inspired Wilson-Cowan-type equations for orientation-dependent contrast perception modelling. Journal of Mathematical Imaging and Vision. contrastPerception.pdf (1.68 MB)
Bertalmío M, Calatroni L, Franceschi V, Franceschiello B, Gomez-Villa A, Prandi D.  2020.  Visual illusions via neural dynamics: Wilson-Cowan-type models and the efficient representation principle. Journal of Neurophysiology. Cowan.pdf (1.31 MB)
Bertalmío M, Cyriac P, Batard T, Martinez-García M, Malo J.  2017.  The Wilson-Cowan Model Describes Contrast Response and Subjective Distortion. Vision Sciences Society Annual Meeting. VSS_2017_M.pdf (384.41 KB)
Batard T, Bertalmío M.  2013.  Harmonic Flow for Histogram Matching. Geometric Computation for Computer Vision GCCV, Guanajuato, Mexico. GCCV2013.pdf (3.6 MB)
Batard T, Bertalmío M.  2013.  Generalized Gradient on Vector Bundle - Application to Image Denoising. Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM-2013), Austria. CameraReady_Paper.pdf (2.04 MB)
Batard T, Bertalmío M.  2015.  Duality Principle for Image Regularization and Perceptual Color Correction Models. Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). CameraReady.pdf (858.33 KB)
Batard T, Bertalmío M.  2018.  A Geometric Model of Brightness Perception and its Application to Color Images Correction. *Accepted* in Journal of Mathematical Imaging and Vision. Manuscript.pdf (2.42 MB)
Batard T, Bertalmío M.  2014.  On Covariant Derivatives and Their Applications to Image Regularization. SIAM Journal on Imaging Sciences (SIIMS). SIIMS_Denoising.pdf (4 MB)
Batard T, Maldonado ERamon, Steidl G, Bertalmío M.  2019.  A connection between image processing and artificial neural networks layers through a geometric model of visual perception. Scale Space and Variational Methods in Computer Vision (SSVM2019). geometricModelPerception.pdf (13.46 MB)
Batard T, Bertalmío M.  2016.  A Class of Nonlocal Variational Problems on a Vector Bundle for Color Image Local Contrast Reduction/Enhancement. *Accepted* in Geometry, Imaging and Computing. preprint2.pdf (11.12 MB)

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