Rafał Mantiuk *
Rafał Mantiuk
Reader in Graphics and Displays
Department of Computer Science and Technology
The Computer Laboratory
Rainbow Research Group
University of Cambridge

Office address
University of Cambridge
Computer Laboratory
William Gates Building
15 JJ Thomson Avenue
Cambridge CB3 0FD
United Kingdom
office: +44 1223 763831
rafal [dot] mantiuk [at] cl [dot] cam [dot] ac [dot] uk

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If you are contacting me about internship, PhD studentship, or a PostDoc position, please check the "Jobs" section first.

Reserach interest

Applied visual perception; high dynamic range imaging; display algorithms; machine learning for image synthesis; tone-mapping; video coding for new display technologies; image and video quality metrics; visibility metrics; virtual reality and low-level perception; computational photography; computational displays; novel display technologies; colour; perception in computer graphics; novel image and video representations (beyond 2D); psychophysics; modeling visual perception with machine learning.


Reader in Graphics and Displays, University of Cambridge, Computer Laboratory, UK (from 2018)
Senior Lecturer, University of Cambridge, Computer Laboratory, UK (2015-2018)
Lecturer / Senior Lecturer, Bangor University, School of Computer Science, UK (2009-2015)
Postdoc Fellow, University of British Columbia, Canada (2008-2009)
Postdoc, Max-Planck-Institut for Computer Science, Germany (2007-2008)
Internship, Sharp Laboratories of America, Camas WA, USA (2006)
PhD (summa cum laude, Computer Science), Max-Planck-Institut for Computer Science, Germany (2006)
Msc (Computer Science), Technical University of Szczecin, Poland (2003)
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Recent projects

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Transformation Consistency Regularization - A Semi Supervised Paradigm for Image to Image Translation

We can train image-to-image networks in a semi-supervised manner with 50% or less of paired data, or we can improve performance using using large quantities of unpaired data. The method works with multiple tasks, including single-image super-resolution, denoising, semantic segmentation, colourization and others.

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A perceptual model of motion quality for rendering with adaptive refresh-rate and resolution

Is it better to render at 4K or 144Hz? The quality of motion depends on the velocity, the type of eye motion, viewing distance and other factors. We model the influence of all those factors on the perceived quality of motion and use such a model to adaptively select refresh-rate and resolution for rendering.

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Spatio-chromatic conteast sensitivity function for high dynamic range

A luminance and colour contrast sensitivity function has been measured and modelled in the range of luminance from 0.001 cd/m^2 to 10,000 cd/m^2. The new function can predict detection thresholds for three colour directions, a range of frequencies, luminance levels and stimulus sizes.

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Recent papers

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Awards and grants

My contribution to the organization of research networks and conferences can be found here.