1 Media Technology, The Technical Faculty of IT and Design, Aalborg University, VBN2 Visual Analysis of People Laboratory, The Technical Faculty of IT and Design, Aalborg University, VBN3 Department of Architecture, Design and Media Technology, The Technical Faculty of IT and Design, Aalborg University, VBN4 Centre for Mobility and Urban Studies, The Technical Faculty of IT and Design, Aalborg University, VBN5 Mobility and Tracking Technologies, The Technical Faculty of IT and Design, Aalborg University, VBN6 The Faculty of Engineering and Science (TECH), Aalborg University, VBN7 Aalborg U Robotics, The Faculty of Humanities, Aalborg University, VBN
A comprehensive survey
Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new super-resolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.
Machine Vision and Applications, 2014, Vol 25, Issue 6, p. 1423-1468