Image super-resolution for outdoor digital forensics. Usability and legal aspects Villena, Salvador Vega, Miguel Mateos, Javier Rosenberg, Duska Murtagh, Fionn Molina, Rafael Katsaggelos, Aggelos K. Super-resolution Outdoor surveillance Usability Legal aspects This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) through projects TIN2013-43880-R and DPI2016-77869-C2-2-R, the Department of Energy under Grant DE-NA0002520, ONR award N00014-15-1-2735, NSF IDEAS program, DARPA ReImagine. Digital Forensics encompasses the recovery and investigation of data, images, and recordings found in digital devices in order to provide evidence in the court of law. This paper is devoted to the assessment of digital evidence which requires not only an understanding of the scientific technique that leads to improved quality of surveillance video recordings, but also of the legal principles behind it. Emphasis is given on the special treatment of image processing in terms of its handling and explanation that would be acceptable in a court of law. In this context, we propose a variational Bayesian approach to multiple- image super-resolution based on Super-Gaussian prior models that automatically enhances the quality of outdoor video recordings and estimates all the model parameters while preserving the authenticity, credibility and reliability of video data as digital evidence. The proposed methodology is validated both quantitatively and visually on synthetic videos generated from single images and real-life videos and applied to a real-life case of damages and stealing in a private property. 2023-12-18T09:08:42Z 2023-12-18T09:08:42Z 2018-06 journal article S. Villena, M. Vega, J. Mateos, D. Rosenberg, F. Murtagh, R. Molina, A.K. Katsaggelos, “Image super-resolution for outdoor digital forensics. Usability and legal aspects,” Computers in industry, vol. 98, pp. 34–47, 2018. https://doi.org/10.1016/j.compind.2018.02.004 https://hdl.handle.net/10481/86278 10.1016/j.compind.2018.02.004 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ embargoed access Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Elsevier