Publications
Convolutional Neural Networks Deceived by Visual Illusions. *Accepted* in Computer Vision and Pattern Recognition (CVPR).
CNNillusions.pdf (4.05 MB)
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2019. 
Visual Illusions Also Deceive Convolutional Neural Networks: Analysis and Implications. Vision Research.
visualIllusions.pdf (3.73 MB)
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2020. 
Visual information flow in Wilson-Cowan networks. Journal of Neurophysiology.
VisualInformation.pdf (3.12 MB)
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2020. 
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)
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2016. 
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)
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2014. 
A Decomposition Framework for Image Denoising Algorithms. IEEE Transactions on Image Processing.
DenoisingTIP.pdf (10.26 MB)
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2015. 
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)
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2016. 
A variational Framework for Single Image Dehazing. European Conference on Computer Vision Workshops .
Galdranetaleccvw.pdf (9.65 MB)
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2014. 
On the Duality Between Retinex and Image Dehazing. *Accepted* in Computer Vision and Pattern Recognition (CVPR).
ImageDehazing.pdf (4.3 MB)
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2018. 
Enhanced Variational Image Dehazing. SIAM Journal on Imaging Sciences (SIIMS).
VariationalDehazing_EVID_final_LR.pdf (15.17 MB)
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2015. 
A Non Local Variational Formulation for the Improvement of Tone Mapped Images. SIAM Journal on Imaging Sciences (SIIMS).
ToneMapping.pdf (4.68 MB)
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2014. 
Optimized Tone Curve for In-Camera Image Processing. IS&T Electronic Imaging Conference.
EI16_tmo.pdf (9.55 MB)
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2016. 
Vision models fine-tuned by cinema professionals for High Dynamic Range imaging in movies. Multimedia Tools and Applications.
VisionModelsHDR.pdf (2.64 MB)
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2020. 
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)
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2015. 
Perceptual Dynamic Range for In-Camera Image Processing. British Machine Vision Conference (BMVC).
tonemapping_bmvc.pdf (4.59 MB)
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2015. 
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)
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2013. 
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)
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2016. 
A Contrario Selection of Optimal Partitions for Image Segmentation. SIAM Journal on Imaging Sciences (SIIMS).
SIIMS_final.pdf (2.56 MB)
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2013. .
2020. 
Cortical-inspired Wilson-Cowan-type equations for orientation-dependent contrast perception modelling. Journal of Mathematical Imaging and Vision.
contrastPerception.pdf (1.68 MB)
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2020. 
Visual illusions via neural dynamics: Wilson-Cowan-type models and the efficient representation principle. Journal of Neurophysiology.
Cowan.pdf (1.31 MB)
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2020. 
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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2017. 
Connections between Retinex, neural models and variational methods. IS&T Electronic Imaging Conference.
BertalmioRetinex50_EI2016.pdf (152.17 KB)
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2016. 
Denoising an Image by Denoising its Curvature Image. SIAM Journal on Imaging Sciences (SIIMS).
siimsRR6a.pdf (5.34 MB)
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2013. 
Evidence for the intrinsically nonlinear nature of receptive fields in vision. Scientific reports.
NLreceptiveFields.pdf (2.65 MB)
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2020. 