Andreas Aakerberg

Andreas Aakerberg is an Assistant Professor at Aalborg University's Department of Architecture, Design and Media Technology within the Technical Faculty of IT and Design. His research focuses on Deep Learning applications in Image and Video Enhancement, particularly for Forensics, Surveillance, and Computational Photography. He is affiliated with the Visual Analysis and Perception and AI for the People research groups. His work contributes to UN Sustainable Development Goals through advancements in technology and accessibility.

Key research interests include Super-Resolution techniques, Low-Light Image Enhancement, and Generative AI. He has developed datasets like RELLISUR and Spatially Variant Super-Resolution (SVSR) to advance real-world applications. His publications address challenges in surveillance systems, thermal imaging, and semantic segmentation. Aakerberg teaches Deep Learning, MLOps, and Image Processing, emphasizing practical, industry-relevant skills.

Notable contributions include the PDA-RWSR method for pixel-wise degradation adaptation and the RELIEF framework for joint low-light enhancement and super-resolution using transformers. His work bridges theoretical advancements with real-world usability, such as improving face-image resolution from surveillance cameras.