University of Nantes
Technical University of Denmark
Bangor University
University of Cambridge
University of Oxford
Max Planck Institute for Informatics
Force Technology
Fraunhofer IIS
DxO
The French National Center for Scientific Research
Bang & Olufsen
ESR1 Waqas Ellahi
ESR2 Ali Ak
ESR3 Milan Stepanov
ESR4 Abhishek Goswami
ESR5 Hanxue Liang
ESR6 Fangcheng Zhong
ESR7 Akshay Jindal
ESR8 Anantha Krishnan
ESR9 Allie Hexley
ESR10 Krzysztof Wolski
ESR11 Muhammad Shahzeb Khan Gul
ESR12 Jingyu Liu
ESR13 Muhammad Umair Mukati
ESR14 Sarvesh Rajesh Agrawal
ESR15 Randy Frans Fela
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For 15 years, DxO Labs has developed some of the world’s most advanced image processing technologies, which have enabled over 400 million devices to capture the highest quality images achievable.
We develop image processing software for Mac and PC, acclaimed by top photographers the world over and regularly receiving international awards (TIPA, EISA):
All these software solutions are based on our unique know-how in the fields of measuring and calibrating photographic gear, scientific analysis of RAW images, and sophisticated image processing algorithms. We acquired this know-how during 15 years of close collaboration with leading public research laboratories.
DxO’s Image Science Team is the creator of the processing engine at the heart of all our software. Since our creation in 2003, this team has been continuously hosting PhD students. They work together with researchers and engineers to conceive state-of-the-art algorithms and stunning new features. Their creativity allows DxO to remain on top of a very competitive market. Recent achievements include our unrivaled PRIME raw denoising, innovative multi-image processing for the DxO ONE and automatic perspective correction based on AI and image analysis.
“Image quality” traditionally refers to accurately reproducing the scene. As the industry gets better and better at that, our next goal is to enhance the image beyond fidelity, targeting the photographer’s biased perception of the scene and the subject. During the RealVision project, we aim at exploring this for the problem of tone mapping, taking advantage of recent advances in computer vision and machine learning.