- Research Article
- Open Access
Performance Evaluation of Super-Resolution Reconstruction Methods on Real-World Data
EURASIP Journal on Advances in Signal Processingvolume 2007, Article number: 043953 (2007)
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)).
Park SC, Park MK, Kang MG: Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine 2003,20(3):21–36. 10.1109/MSP.2003.1203207
Farsiu S, Robinson MD, Elad M, Milanfar P: Advances and challenges in super-resolution. International Journal of Imaging Systems and Technology 2004,14(2):47-57. 10.1002/ima.20007
Baker S, Kanade T: Limits on super-resolution and how to break them. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002,24(9):1167-1183. 10.1109/TPAMI.2002.1033210
Lin Z, Shum H-Y: Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004,26(1):83-97. 10.1109/TPAMI.2004.1261081
Robinson MD, Milanfar P: Statistical performance analysis of super-resolution. IEEE Transactions on Image Processing 2006,15(6):1413-1428.
Bijl P, Valeton JM: Triangle orientation discrimination: the alternative to minimum resolvable temperature difference and minimum resolvable contrast. Optical Engineering 1998,37(7):1976-1983. 10.1117/1.601904
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.
Pham TQ, Bezuijen M, van Vliet LJ, Schutte K, Luengo Hendriks CL: Performance of optimal registration estimators. Visual Information Processing XIV, March 2005, Orlando, Fla, USA, Proceedings of SPIE 5817: 133–144.
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.
Kay SM: Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice-Hall, Upper Saddle River, NJ, USA; 1993.
Elad M, Hel-Or Y: A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur. IEEE Transactions on Image Processing 2001,10(8):1187-1193. 10.1109/83.935034
Lertrattanapanich S, Bose NK: High resolution image formation from low resolution frames using Delaunay triangulation. IEEE Transactions on Image Processing 2002,11(12):1427-1441. 10.1109/TIP.2002.806234
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.
Hardie RC, Barnard KJ, Bognar JG, Armstrong EE, Watson EA: High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system. Optical Engineering 1998,37(1):247-260. 10.1117/1.601623
Farsiu S, Robinson MD, Elad M, Milanfar P: Fast and robust multiframe super resolution. IEEE Transactions on Image Processing 2004,13(10):1327-1344. 10.1109/TIP.2004.834669
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.
Knutsson H, Westin C-F: Normalized and differential convolution. Proceedings of IEEE Society Conference on Computer Vision and Pattern Recognition (CVPR '93), June 1993, New York, NY, USA 515–523.
Haralick RM, Watson L: A facet model for image data. Computer Graphics and Image Processing 1981,15(2):113-129. 10.1016/0146-664X(81)90073-3
Johnson J: Analysis of image forming systems. Proceedings of Image Intensifier Symposium, October 1958, Fort Belvoir, Va, USA 249–273.
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.
van Vliet LJ, Verbeek PW: Better geometric measurements based on photometric information. Proceedings of IEEE Instrumentation and Measurement Technology Conference (IMTC '94), May 1994, Hamamatsu, Japan 3: 1357–1360.
Pham TQ: Spatiotonal adaptivity in super-resolution of under-sampled image sequences, Ph.D. thesis. Quantitative Imaging Group, TU Delft, Delft, The Netherlands; 2006.