Computer Graphics and Image Processing

Computer generated image of a living room

The computer graphics and image processing group is driving a number of research projects directed towards the development of theory and methodology for image capture, image analysis and image synthesis.  

A common theme within our projects is to develop algorithms and techniques for measuring and digitizing real environments, lighting conditions and material properties so that this information can be used to simulate the interaction between light and matter in a scene to create photo-realistic computer graphics images.

With a strong foundation in theoretically oriented research, the group is active within a number of demonstrator projects and industrial and academic collaborations directed towards development of state-of-the-art applications within the focus areas. We are currently working with projects directed towards:

  • New algorithms and methodologies for photo realistic image synthesis based on Monte Carlo integration
  • High Dynamic Range (HDR) imaging and video capture and statistical image reconstruction
  • Tone mapping and compression of HDR images and video
  • Capture, processing and Light field imaging
  • Appearance capture and modelling of material properties for photo-realistic image synthesis
  • Algorithms and methodology for capture and reconstruction of lighting, geometry and material properties of real scenes based on sensor data

News

Latest publications

2026

Behnaz Kavoosighafi, Maria Eidenskog, Wiktoria Glad, Katerina Vrotsou (2026) An empirical benchmark of deep time-series models for smart meter energy forecasting Energy and AI, Vol. 25, p. 1-12, Article 100870 (Article in journal) https://dx.doi.org/10.1016/j.egyai.2026.100870
Nithesh Chandher Karthikeyan, Jonas Unger, Gabriel Eilertsen (2026) Evaluating representation conditioned diffusion models: A comparative study of image-based representation encoders Journal of Visual Communication and Image Representation, Vol. 121, Article 104950 (Article in journal) https://dx.doi.org/10.1016/j.jvcir.2026.104950
Jiarong Gong, Jonas Unger, Ehsan Miandji (2026) Smaller and Faster 3DGS via Post-Training Dictionary Learning EG 2026 - POSTERS (Conference paper) https://dx.doi.org/10.2312/egp.20261016
Jens Nilsson, Jonas Unger, Gabriel Eilertsen (2026) Multi-Agent Reinforcement Learning for Conflict Resolution in Air Traffic Control With Delayed Actions IEEE Open Journal of Intelligent Transportation Systems, Vol. 7, p. 2037-2044 (Article in journal) https://dx.doi.org/10.1109/ojits.2026.3718458
Behnaz Kavoosighafi, Maria Eidenskog, Wiktoria Glad, Katerina Vrotsou (2026) An empirical benchmark of deep time-series models for smart meter energy forecasting Energy and AI, Vol. 25, Article 100870 (Article in journal) https://dx.doi.org/10.1016/j.egyai.2026.100870
Nithesh Chandher Karthikeyan, Jonas Unger, Gabriel Eilertsen (2026) Representation-Conditioned Diffusion Models for Guided Training Data Generation Synthetic Data for Computer Vision Workshop (SynData4CV) (Conference paper)
Yifan Ding, Liu Xixi, Jonas Unger, Gabriel Eilertsen (2026) Enhancing out-of-distribution detection with extended logit normalization Proceedings of IEEE/CVF Conference on Computer vision and Pattern Recognition (CVPR) (Conference paper)
Glenda Amaral, Stefania Costantini, Giovanni De Gasperis, Lorenzo De Lauretis, Pierangelo Dellacqua, Giancarlo Guizzardi, Francesco Gullo, Andrea Rafanelli (2026) Combining Neural Empathy-Aware Behavior Trees With Knowledge Graphs for Affective Human-AI Teaming IEEE Transactions on Affective Computing, Vol. 17, p. 1328-1343 (Article in journal) https://dx.doi.org/10.1109/TAFFC.2025.3641641
Farina Tariq, Erik Ylipää, Lili Jiang, Patrik Ryden, Patrik L. Andersson (2026) A Benchmark Evaluation of Chemical Structure Extraction from Patents: Insights and Challenges in Chemical Structure Recognition Chemical Research in Toxicology, Vol. 39, p. 1349-1356 (Article in journal) https://dx.doi.org/10.1021/acs.chemrestox.6c00057
Tahereh Dehdarirad, Gabriel Eilertsen, Ericka Johnson, Saghi Hajisharif (2026) Individually fair representation learning for DINOv2 Discover Artificial Intelligence, Vol. 6, Article 515 (Article in journal) https://dx.doi.org/10.1007/s44163-026-01490-y

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