Abstract
Motion blur and signal noise are probably the two most dominant sources of image quality degradation in digital imaging. In low light conditions, the image quality is always a tradeoff between motion blur and noise. Long exposure time is required in low illumination level in order to obtain adequate signal to noise ratio. On the other hand, risk of motion blur due to tremble of hands or subject motion increases as exposure time becomes longer. Loss of image brightness caused by shorter exposure time and consequent underexposure can be compensated with analogue or digital gains. However, at the same time also noise will be amplified. In relation to digital photography the interesting question is: What is the tradeoff between motion blur and noise that is preferred by human observers? In this paper we explore this problem. A motion blur metric is created and analyzed. Similarly, necessary measurement methods for image noise are presented. Based on a relatively large testing material, we show experimental results on the motion blur and noise behavior in different illumination conditions and their effect on the perceived image quality.
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References
Ben-Ezra, M., Nayat, S.K.: Motion based motion deblurring. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(6), 689–698 (2004)
Cho, S., Matsushita, Y., Lee, S.: Removing non-uniform motion blur from images (2007)
Foi, A., Alenius, S., Katkovnik, V., Egiazatrian, K.: Noise measurement for raw-data of digital imaging sensors by automatic segmentation of non-uniform targets. IEEE Sensors Journal 7(10), 1456–1461 (2007)
Guo, Z., Hall, R.W.: Parallel Thinning with Two-Subiteration Algorithms. Communications of the ACM 32(3), 359–373 (1989)
Hytti, H.T.: Characterization of digital image noise properties based on RAW data. In: Proceedings of SPIE, vol. 6059, pp. 86–97 (2006)
James, H., Steven, W.: Local scale control for edge detection and blur estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 699–716 (1996)
Janesick, J.: Scientific Charge Coupled Devices, vol. PM83 (2001)
Kurimo, E.: Motion blur and signal noise in low light imaging, Master Thesis, Helsinki University of Technology, Faculty of Electronics, Communications and Automation, Department of Information and Computer Science (2008)
Liu, X., Gamal, A.E.: Simultaneous image formation and motion blur restoration via multiple capture,....
Marziliano, P., Dufaux, F., Winkler, S., Ebrahimi, T., Genimedia, S.A., Lausanne, S.: A no-reference perceptual blur metric. In: Proceedings of International Conference on Image Processing, vol. 3 (2002)
Nikkanen, J., Kalevo, O.: Menetelmä ja järjestelmä digitaalisessa kuvannuksessa valotuksen säätämiseksi ja vastaava laite. Patent FI 116246 B (2003)
Nikkanen, J., Kalevo, O.: Exposure of digital imaging. Patent application PCT/FI2004/050198 (2004)
Rav-Acha, A., Peleg, S.: Two motion blurred images are better than one. Pattern Recognition letters 26, 311–317 (2005)
Tong, H., Li, M., Zhang, H., Zhang, C.: Blur detection for digital images using wavelet transform. In: Proceedings of IEEE International Conference on Multimedia and Expo., vol. 1 (2004)
Wiener, N.: Extrapolation, interpolation, and smoothing of stationary time series (1992)
Xiao, F., Silverstein, A., Farrell, J.: Camera-motion and effective spatial resolution. In: International Congress of Imaging Science, Rochester, NY (2006)
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Kurimo, E., Lepistö, L., Nikkanen, J., Grén, J., Kunttu, I., Laaksonen, J. (2009). The Effect of Motion Blur and Signal Noise on Image Quality in Low Light Imaging. In: Salberg, AB., Hardeberg, J.Y., Jenssen, R. (eds) Image Analysis. SCIA 2009. Lecture Notes in Computer Science, vol 5575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02230-2_9
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DOI: https://doi.org/10.1007/978-3-642-02230-2_9
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