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Bertalmío M, Levine S.  2012.  "Denoising an image by denoising its curvature image", IMA Preprint.
Bertalmío M, Levine S.  2013.  Denoising an Image by Denoising its Curvature Image. SIAM Journal on Imaging Sciences (SIIMS). siimsRR6a.pdf (5.34 MB)
Bertalmío M.  2014.  Image Processing for Cinema. Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series.
Bertalmío M.  2014.  From Image Processing to Computational Neuroscience: A Neural Model Based on Histogram Equalization. Frontiers in Computational Neuroscience. 8(71)
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.  2016.  Connections between Retinex, neural models and variational methods. IS&T Electronic Imaging Conference. BertalmioRetinex50_EI2016.pdf (152.17 KB)
Bertalmío M, Batard T, Kim J.  2016.  Correcting for Induction Phenomena on Displays of Differrent Size. Vision Sciences Society Annual Meeting.
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)
Bertalmío M, Levine S.  2013.  A Variational Approach for the Fusion of Exposure Bracketed Pairs. IEEE Transactions on Image Processing. fusionOfExposure.pdf (24.67 MB)
Bertalmío M, Calatroni L, Franceschi V, Franceschiello B, Prandi D.  2019.  A cortical-inspired model for orientation-dependent contrast perception: a link with Wilson-Cowan equations. Scale Space and Variational Methods in Computer Vision (SSVM2019). ContrastPerception.pdf (669.04 KB)