Abstract
Several images (a sequence) may be used to obtain better image quality. This method is perfect for super-resolution algorithms, which improve sub-pixel clarity of the image and allow a more detailed view. It is possible that illumination variations, e.g. those caused by a light source, lessen the benefits of super-resolution algorithms. The reduction of the quantity of such occurrences by stabilizing variations is important. An enhanced stabilization algorithm is proposed for purposes of reduction of variations in illumination. It is based on the energy contained in wavelet coefficients. In the proposed algorithm, energy plays a role of the memory buffer in memory-based techniques of illumination variation reduction. The benefits of the proposed image stabilization are the higher quality of images and better visualization. Possible applications are in surveillance, product quality control, engine monitoring, corrosion monitoring, micro/nano microscopy, etc.
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References
Amer, A.: Memory-based spatio-temporal real-time object segmentation for video surveillance. In: Proceedings of the Conference on Real-time Imaging VII, Santa Clara, CA, vol. 5012, pp. 10–21. 22–23 Jan 2003
Zhichao, L., Joo, E.M.: Face recognition under varying illumination. In: Er, M.J. (ed.) New Trends in Technologies: Control, Management, Computational Intelligence and Network Systems, InTech, Rijeka (2010)
Perronnin, F., Dugelay, J.L.: A model of illumination variation for robust face recognition. Workshop on multimodal user authentication, Santa Barbara, USA, 11–12 Dec 2003
Eekeren, A.W.M., Schutte, K., Vliet, L.J.: Multiframe super-resolution reconstruction of small moving objects. IEEE Trans. Image Process 19, 2901–2912 (2010)
Robinson, M.D., Toth, C.A., Lo, J.Y., Farsiu, S.: Efficient fourier-wavelet super-resolution. IEEE Trans. Image Process 19, 2669–2681 (2010)
He, Y., Yap, K.H., Chen, L., Chau, L.P.: A nonlinear least square technique for simultaneous image registration and super-resolution. IEEE Trans. Image Process 16, 2830–2841 (2007)
Brito, A.E., Chan, S.H., Cabrera, S.D.: SAR image superresolution via 2-D adaptive extrapolation. Multidimension. Syst. Signal Process. 14, 83–104 (2003)
Ng, M.K., Yau, A.C.: Super-resolution image restoration from blurred low-resolution images. J Math Imaging Vis 23, 367–378 (2005)
Vandewalle, P.: Super-resolution from unregistered aliased images. Ph.D. thesis, École Polytechnique Fédérale De Lausanne (2006)
Nguyen, N., Milanfar, P.: A wavelet-based interpolation-restoration method for superresolution (wavelet superresolution). Circ. Syst. Signal Process. 19, 321–338 (2000)
Bose, N.K.: Image phase-only information for landmine classification using ANN and DT/wavelet superresolution from image sequence. Sixth Annual Army Landmine Research Technical Review Meeting, Springfield, VA, 23 Jan 2003
Mastriani, M.: New wavelet-based superresolution algorithm for speckle reduction in SAR images. Int. J. Comp. Sci. 1, 291–298 (2006)
Bose, N.K., Letrattanapanich, S., Chappalli, M.B.: Superresolution with second generation wavelets. Signal Process. Image 19, 387–391 (2004)
Bose, N.K., Chappalli, M.B.: A second-generation wavelet framework for super-resolution with noise filtering. Int. J. Imaging Syst. Technol. 14, 84–89 (2004)
Rosin, P., Ioannidis, E.: Evaluation of global image thresholding for change detection. Pattern Recogn. Lett. 24, 2345–2356 (2003)
Porter, R., Fraser, A.M., Hush, D.: Wide-area motion imagery. IEEE Signal Process. Mag. 27, 56–65 (2010)
Dorf, R.C.: The Electrical Engineering Handbook. CRC Press LLC, Boca Raton (2000)
Mallat, S.: A Wavelet Tour of Signal Processing, 2nd edn. edn. Academic Press, New York (1999)
Poularikas, A.D.: Signals and Systems Primer with Matlab. CRC Press, New York (2007)
Mertins, A.: Signal Analysis: Wavelets, Filter Banks Time-Frequency Transforms and Applications. Wiley, West Sussex (1999)
Eekeren, A.W.M., Schutte, K., Vliet, L.J.: Multiframe Super-Resolution Reconstruction of Small Moving Objects. IEEE Trans. Image Process. 19, 2901–2912 (2010)
Vujović, I., Kuzmanić, I., Vujović, M.: Algorithm for combined wavelet quasi-superresolution. In: Proceedings of 5th International Symposium Communication Systems Networks and Digital Signal Processing, Patras, Greece, vol. 1, pp. 469–473, 19–21 July 2006
Vujović, I., Kuzmanić, I.: Wavelet quasi-superresolution in marine applications. In: Proceedings of the 48th International Symposium ELMAR—2006 focused on Multimedia Signal Processing and Communications, Zadar, Croatia, vol. 1, pp. 65–68 (2006)
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Kuzmanić, I., Beroš, S.M., Šoda, J., Vujović, I. (2013). Pre-Processing for Image Sequence Visualization Robust to Illumination Variations. In: Öchsner, A., da Silva, L., Altenbach, H. (eds) Design and Analysis of Materials and Engineering Structures. Advanced Structured Materials, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32295-2_4
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DOI: https://doi.org/10.1007/978-3-642-32295-2_4
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