2018/12/21 by Thomas Köhler, Köhler, Thomas
Computer Science · Engineering · Physics and Astronomy · #Advanced Image Processing Techniques #Advanced Optical Sensing Technologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Processing Techniques and Applications #Image and Signal Denoising Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1812.09375
Ph.D. thesis at the Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg; source code is available at https://www5.cs.fau.de/de/forschung/software/multi-frame-super-resolution-toolbox/ . https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/9145
arxiv created 2018/12/21 · openalex publication_date 2018/12/21 · arxiv updated 2018/12/27 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
The optical resolution of a digital camera is one of its most crucial parameters with broad relevance for consumer electronics, surveillance systems, remote sensing, or medical imaging. However, resolution is physically limited by the optics and sensor characteristics. In addition, practical and economic reasons often stipulate the use of out-dated or low-cost hardware. Super-resolution is a class of retrospective techniques that aims at high-resolution imagery by means of software. Multi-frame algorithms approach this task by fusing multiple low-resolution frames to reconstruct high-resolution images. This work covers novel super-resolution methods along with new applications in medical imaging.