TY  - CONF
AB  - Multi-frame super-resolution algorithms aim to increase spatial resolution by fusing information from several low-resolution perspectives of a scene. While a wide array of super-resolution algorithms now exist, the comparative capability of these techniques in practical scenarios has not been adequately explored. In addition, a standard quantitative method for assessing the relative merit of super-resolution algorithms is required. This paper presents a comprehensive practical comparison of existing super-resolution techniques using a shared platform and 4 common greyscale reference images. In total, 13 different super-resolution algorithms are evaluated, and as accurate alignment is critical to the super-resolution process, 6 registration algorithms are also included in the analysis. Pixel-based visual information fidelity (VIFP) is selected from the 12 image quality metrics reviewed as the measure most suited to the appraisal of super-resolved images. Experimental results show that Bayesian super-resolution methods utilizing the simultaneous autoregressive (SAR) prior produce the highest quality images when combined with generalized stochastic Lucas-Kanade optical flow registration.
AU  - Nelson, Kyle
AU  - Bhatti, Asim
AU  - Nahavandi, Saeid
C2  - 2012
CY  - Fremantle, Australia
DA  - 3-5 December 2012
KW  - super-resolution, multi-frame, image enhancement, image quality, performance evaluation, comparison
LA  - English
N1  - ERA B
PY  - 2012
ST  - Performance Evaluation of Multi-frame Super-resolution Algorithms
T2  - International Conference on Digital Image Computing: Techniques and Applications (DICTA)
TI  - Performance Evaluation of Multi-frame Super-resolution Algorithms
ID  - 1141868
ER  -