Performance Evaluation of Super-Resolution Reconstruction Methods on Real-World Data
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The performance of a super-resolution (SR) reconstruction method on real-world data is not easy to measure, especially as a ground-truth (GT) is often not available. In this paper, a quantitative performance measure is used, based on triangle orientation discrimination (TOD). The TOD measure, simulating a real-observer task, is capable of determining the performance of a specific SR reconstruction method under varying conditions of the input data. It is shown that the performance of an SR reconstruction method on real-world data can be predicted accurately by measuring its performance on simulated data. This prediction of the performance on real-world data enables the optimization of the complete chain of a vision system; from camera setup and SR reconstruction up to image detection/recognition/identification. Furthermore, different SR reconstruction methods are compared to show that the TOD method is a useful tool to select a specific SR reconstruction method according to the imaging conditions (camera's fill-factor, optical point-spread-function (PSF), signal-to-noise ratio (SNR)).
KeywordsInformation Technology Input Data Performance Evaluation Vision System Simulated Data
- 7.Bijl P, Schutte K, Hogervorst MA: Applicability of TOD, MTDP, MRT and DMRT for dynamic image enhancement techniques. Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVII, April 2006, Kissimmee, Fla, USA, Proceedings of SPIE 6207: 1-12.Google Scholar
- 9.Lucas BD, Kanade T: An iterative image registration technique with an application to stereo vision. Proceedings of the DARPA Image Understanding Workshop, April 1981, Washington, DC, USA 121-130.Google Scholar
- 13.Kaltenbacher E, Hardie RC: High resolution infrared image reconstruction using multiple, low resolution, aliased frames. Proceedings of IEEE National Aerospace and Electronics Conference (NAECON '96), May 1996, Dayton, Ky, USA 2: 702-709.Google Scholar
- 16.Pham TQ, van Vliet LJ, Schutte K: Robust fusion of irregularly sampled data using adaptive normalized convolution. EURASIP Journal on Applied Signal Processing 2006, 2006: 12 pages.Google Scholar
- 19.Johnson J: Analysis of image forming systems. Proceedings of Image Intensifier Symposium, October 1958, Fort Belvoir, Va, USA 249-273.Google Scholar
- 20.Valeton JM, Bijl P, Agterhuis E, Kriekaard S: T-CAT, a new thermal camera acuity tester. Infrared Imaging Systems: Design, Analysis, Modelling, and Testing XI, April 2000, Orlando, Fla, USA, Proceedings of SPIE 4030: 232-238.Google Scholar
- 22.Pham TQ: Spatiotonal adaptivity in super-resolution of under-sampled image sequences, Ph.D. thesis. Quantitative Imaging Group, TU Delft, Delft, The Netherlands; 2006.Google Scholar
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