Photo of Jonas Unger

Jonas Unger


Research leader – Computer Graphics and Image Processing


Professor Jonas Unger is leading the research efforts within Computer Graphics and Image Processing group in the division for Media and Information Technology at the department of Science and Technology. The vision of the group is to research and develop new theory and technology for computational imaging by fusing computer graphics,vision and sensors with human perception and machine learning to capture, digitize and reason about aspects of the world that have not been possible before. With a strong foundation in the theoretically oriented research, the group is active within a number of industrial and academic collaborations directed towards development of state-of-the-art applications ranging from 3D-reconstruction of scenes, photorealistic image synthesis and digitization of optical material properties to computer vision for heart surgery, AI driven diagnostics tools for medical applications, perceptual display algorithms, and software for autonomous systems such as self-driving cars and robot navigation.

A collage with examples of different technics used in computer graphics



Jens Nilsson, Jonas Unger (2023) Swedish civil air traffic control dataset Data in Brief, Vol. 48, Article 109240 Continue to DOI
Behnaz Kavoosighafi, Jeppe Revall Frisvad, Saghi Hajisharif, Jonas Unger, Ehsan Miandji (2023) SparseBTF: Sparse Representation Learning for Bidirectional Texture Functions
Katerina Vrotsou, Carlo Navarra, Kostiantyn Kucher, Igor Fedorov, Fredrik Schück, Jonas Unger, Tina-Simone Neset (2023) Towards a Volunteered Geographic Information-Facilitated Visual Analytics Pipeline to Improve Impact-Based Weather Warning Systems Atmosphere, Vol. 14, Article 1141 Continue to DOI


Tanaboon Tongbuasirilai, Jonas Unger, Christine Guillemot, Ehsan Miandji (2022) A Sparse Non-parametric BRDF Model ACM Transactions on Graphics, Vol. 41, Article 181 Continue to DOI
Karin Stacke, Jonas Unger, Claes Lundström, Gabriel Eilertsen (2022) Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications The Journal of Machine Learning for Biomedical Imaging, Vol. 1, Article 023