Publications

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G
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)
Gomez-Villa A, Martín A, Vazquez-Corral J, Bertalmío M, Malo J.  2020.  Visual Illusions Also Deceive Convolutional Neural Networks: Analysis and Implications. Vision Research. visualIllusions.pdf (3.73 MB)
Gomez-Villa A, Bertalmío M, Malo J.  2020.  Visual information flow in Wilson-Cowan networks. Journal of Neurophysiology. VisualInformation.pdf (3.12 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)
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)
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)
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)
C
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)
Cyriac P, Canham T, Kane D, Bertalmío M.  2020.  Vision models fine-tuned by cinema professionals for High Dynamic Range imaging in movies. Multimedia Tools and Applications. VisionModelsHDR.pdf (2.64 MB)
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)
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, 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)
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.  2016.  Connections between Retinex, neural models and variational methods. IS&T Electronic Imaging Conference. BertalmioRetinex50_EI2016.pdf (152.17 KB)
Bertalmío M, Gomez-Vila A, Martín A, Vazquez-Corral J, Kane D, Malo J.  2020.  Evidence for the intrinsically nonlinear nature of receptive fields in vision. Scientific reports. NLreceptiveFields.pdf (2.65 MB)

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