Publications
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. 
Enhanced Variational Image Dehazing. SIAM Journal on Imaging Sciences (SIIMS).
VariationalDehazing_EVID_final_LR.pdf (15.17 MB)
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2015. 
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. 
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. 
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. 
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. 
Natural Images Statistics as Function of Dynamic Range. Vision Sciences Society Annual Meeting.
Abstract.pdf (6.42 MB)
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2017. 
Statistics of natural images as a function of dynamic range. *Accepted* in Journal of Vision.
StatisticsNaturalImages.pdf (2.76 MB)
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2019. 
Scale invariance does not hold for high dynamic range images, but is reestablished by early retinal nonlinearities. The European Conference on Visual Perception (ECVP).
poster_ECVP.pdf (2.95 MB)
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2017. 
Dynamic range, light scatter in the eye and perceived image quality. European Conference on Visual Perception .
PosterECVP.pdf (16.48 MB)
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2015. 
System gamma as a function of image- and monitor- dynamic range. Journal of Vision.
JOV.pdf (1.86 MB)
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2015. 
A re-evaluation of Whittle (1986, 1992) reveals the link between detection thresholds, discrimination thresholds and brightness perception. *Accepted* in Journal of Vision.
evaluationWhittle.pdf (4.05 MB)
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2019. 
The impact of ‘crispening’ upon the perceived contrast of textures. The Optical Society of America.
OSA2015_Poster.pdf (710.24 KB)
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2015. 
Is There A Preference For Linearity When Viewing Natural Images? SPIE/IS&T Electronic Imaging Conference.
IsThereAPreferenceForLinearityWhenViewingNaturalScenes.pdf (2.2 MB)
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2015. 
The influence of lightness, and the crispening effect on the per- ceived contrast of textured images. IS&T Electronic Imaging Conference.
EI2016_LightnessAndCrispening.pdf (1.41 MB)
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2016. 
Explaining 'Crispening' as a Gain Control Mechanism.. Vision Sciences Society Annual Meeting.
VSS_supporting_page.pdf (901.01 KB)
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2017. 