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@INPROCEEDINGS{1141868,
  author = {Kyle Nelson, Asim Bhatti, Saeid Nahavandi},
  title = {Performance Evaluation of Multi-frame Super-resolution Algorithms},
  booktitle = {International Conference on Digital Image Computing: Techniques and Applications (DICTA)},
  year = {2012},
  volume = {2012},
  address = {Fremantle, Australia},
  month = {December},
  organization = {Centre for Intelligent Systems Research, Deakin University, Geelong,
	Victoria, Australia},
  abstract = {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.},
  keywords = {super-resolution, multi-frame, image enhancement, image quality, performance evaluation, comparison}
}