Publications

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[ Author(Asc)] Title Type Year
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G
Gomez-Villa A, Bertalmío M, Malo J.  2020.  Visual information flow in Wilson-Cowan networks. Journal of Neurophysiology. VisualInformation.pdf (3.12 MB)
Gomez-Villa A, Martín A, Vazquez-Corral J, Bertalmío M.  2019.  Convolutional Neural Networks Deceived by Visual Illusions. *Accepted* in Computer Vision and Pattern Recognition (CVPR). CNNillusions.pdf (4.05 MB)
Ghimpeteanu G, Batard T, Seybold T, Bertalmío M.  2016.  Local denoising applied to RAW images may outperform non-local patch-based methods applied to the camera output. IS&T Electronic Imaging Conference. DenoisingEI2016.pdf (2.09 MB)
Ghimpeteanu G, Kane D, Batard T, Levine S, Bertalmío M.  2016.  Local Denoising Based on Curvature Smoothing can Visually Outperform Non-local Methods on Photographs with Actual Noise. IEEE International Conference on Image Processing. DenoisingICIP2016.pdf (1.9 MB)
Ghimpeteanu G, Batard T, Bertalmío M, Levine S.  2014.  Denoising an Image by Denoising its Components in a Moving Frame. International Conference on Image and Signal Processing (ICISP). *Best Paper Award*. icisp00.pdf (6.88 MB)
Ghimpeteanu G, Batard T, Bertalmío M, Levine S.  2015.  A Decomposition Framework for Image Denoising Algorithms. IEEE Transactions on Image Processing. DenoisingTIP.pdf (10.26 MB)
Galdran A, Alvarez-Gila A, Bria A, Vazquez-Corral J, Bertalmío M.  2018.  On the Duality Between Retinex and Image Dehazing. *Accepted* in Computer Vision and Pattern Recognition (CVPR). ImageDehazing.pdf (4.3 MB)
Galdran A, Vazquez-Corral J, Pardo D, Bertalmío M.  2015.  Enhanced Variational Image Dehazing. SIAM Journal on Imaging Sciences (SIIMS). VariationalDehazing_EVID_final_LR.pdf (15.17 MB)
Galdran A, Vazquez-Corral J, Pardo D, Bertalmío M.  2014.  A variational Framework for Single Image Dehazing. European Conference on Computer Vision Workshops . Galdranetaleccvw.pdf (9.65 MB)
C
Cyriac P, Bertalmío M, Kane D, Vazquez-Corral J.  2015.  A Tone Mapping Operator Based on Neural and Psychophysical Models of Visual Perception. Proc. SPIE Human Vision and Electronic Imaging XX. ToneMappingSPIE.pdf (3.59 MB)
Cyriac P, Kane D, Bertalmío M.  2015.  Perceptual Dynamic Range for In-Camera Image Processing. British Machine Vision Conference (BMVC). tonemapping_bmvc.pdf (4.59 MB)
Cyriac P, Batard T, Bertalmío M.  2013.  A Variational Method for the Optimization of Tone Mapping Operators. 6th Pacific-Rim Symposium on Image and Video Technology, Guanajuato, Mexico. TM_PSIVT2013.pdf (5.87 MB)
Cyriac P, Kane D, Bertalmío M.  2016.  Automatic, Viewing-condition Dependent Contrast Grading Based on Perceptual Models. Society of Motion Picture & Television Engineers Annual Technical Conference & Exhibition. ToneMapping_smpte16.pdf (739.66 KB)
Cyriac P, Batard T, Bertalmío M.  2014.  A Non Local Variational Formulation for the Improvement of Tone Mapped Images. SIAM Journal on Imaging Sciences (SIIMS). ToneMapping.pdf (4.68 MB)
Cyriac P, Kane D, Bertalmío M.  2016.  Optimized Tone Curve for In-Camera Image Processing. IS&T Electronic Imaging Conference. EI16_tmo.pdf (9.55 MB)
Cardelino J, Caselles V, Bertalmío M, Randall G.  2013.  A Contrario Selection of Optimal Partitions for Image Segmentation. SIAM Journal on Imaging Sciences (SIIMS). SIIMS_final.pdf (2.56 MB)
Canham T, Vazquez-Corral J, Mathieu E, Bertalmío M.  2020.  Matching visual induction effects on screens of different size by regularizing a neural field model of color appearance. Submitted. visualInductionEffects.pdf (2.63 MB)
B
Bertalmío M, Batard T, Kim J.  2016.  Correcting for Induction Phenomena on Displays of Differrent Size. Vision Sciences Society Annual Meeting.
Bertalmío M.  2014.  Image Processing for Cinema. Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series.
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.  2014.  From Image Processing to Computational Neuroscience: A Neural Model Based on Histogram Equalization. Frontiers in Computational Neuroscience. 8(71)
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, 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)

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