Parametric logarithmic type image processing for contrast based auto-focus in extreme lighting conditions

• Autorzy: Corneliu Florea , Laura Florea
• Języki publikacji: EN

Abstrakty

EN

While most of state-of-the-art image processing techniques were built under the so-called classical linear image processing, an alternative that presents superior behavior for specific applications comes in the form of Logarithmic Type Image Processing (LTIP). This refers to mathematical models constructed for the representation and processing of gray tones images. In this paper we describe a general mathematical framework that allows extensions of these models by various means while preserving their mathematical properties. We propose a parametric extension of LTIP models and discuss its similarities with the human visual system. The usability of the proposed extension model is verified for an application of contrast based auto-focus in extreme lighting conditions. The closing property of the named models facilitates superior behavior when compared with state-of-the-art methods.

Słowa kluczowe

EN

logarithmic image processing; digital camera; auto-focus

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2013
• Tom: 23
• Numer: 3
• Strony: 637-648

Daty

wydano

2013

otrzymano

2012-10-31

poprawiono

2013-02-21

Twórcy

autor

Corneliu Florea

• Image Processing and Analysis Laboratory, University Politehnica of Bucharest, Splaiul Independenţei 313, Bucharest, Romania

autor

Laura Florea

• Image Processing and Analysis Laboratory, University Politehnica of Bucharest, Splaiul Independenţei 313, Bucharest, Romania

Bibliografia

• Byrski, W. and Byrski, J. (2012). The role of parameter constraints in EE and OE methods for optimal identification of continuous LTI models, International Journal of Applied Mathematics and Computer Science 22(2): 379-388, DOI: 10.2478/v10006-012-0028-3.
• Deng, G. (2009). An entropy interpretation of the logarithmic image processing model with application to contrast enhancement, IEEE Transactions on Image Processing 18(5): 1135-1140.
• Deng, G. (2012). A generalized logarithmic image processing model based on the giga-vision sensor model, IEEE Transactions on Image Processing 21(3): 1406-1414.
• Deng, G., Cahill, L.W. and Tobin, G.R. (1995). A study of logarithmic image processing model and its application to image enhancement, IEEE Transactions on Image Processing 4(4): 506-512.
• Fabijańska, A. (2012). A survey of subpixel edge detection methods for images of heat-emitting metal specimens, International Journal of Applied Mathematics and Computer Science 22(3): 695-710, DOI: 10.2478/v10006-012-0052-3.
• Fernandes, M., Gavet, Y. and Pinoli, J.C. (2010). Improving focus measurements using logarithmic image processing, Journal of Microscopy 242(3): 228-241, http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2818.2010.03461.x/abstract.
• Ferwerda, J.A., Pattanaik, S.N., Shirley, P. and Greenberg, D.P. (1996). A model of visual adaptation for realistic image synthesis, SIGGRAPH Conference Proceedings, New Orleans, LA, USA, pp. 249-258.
• Florea, C. and Florea, L. (2011). A parametric non-linear algorithm for contrast based autofocus, Proceedings of the IEEE International Conference on Intelligent Computer Communication and Processing, ICCP, Cluj, Romania, pp. 75-82.
• Florea, C., Vertan, C., Florea, L. and Sultana, A. (2009). Non-linear parametric derivation of contour detectors for cellular images, Proceedings of the IEEE International Symposium on Signals, Circuits and Systems, ISSCS, Iaşi, Romania, Vol. 2, pp. 321-325.
• Hefferon, J. (2008). Linear Algebra, Web edition, http://joshua.smcvt.edu/math/hefferon.html.
• Jourlin, M. and Pinoli, J.C. (1987). Logarithmic image processing, Acta Stereologica 6(1): 651-656.
• Jourlin, M. and Pinoli, J.C. (1988). A model for logarithmic image processing, Journal of Microscopy 149(1): 21-35.
• Jourlin, M. and Pinoli, J.C. (1995). Image dynamic range enhancement and stabilization in the context of the logarithmic image processing model, Signal Processing 41(2): 225-237.
• Kristan, M., Pers, J., Perse, M. and Kovacic, S. (2006). A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform, Pattern Recognition Letters 27(13): 1431-1439.
• Krotkov, E. (1987). Focusing, International Journal of Computer Vision 1(3): 223-237.
• Larson, E.C. and Chandler, D.M. (2010). Most apparent distortion: Full-reference image quality assessment and the role of strategy, Journal of Electronic Imaging 19(1): 011006.
• Lee, S., Yoo, J., Kumar, Y. and Kim, S. (2009). Reduced energy-ratio measure for robust autofocusing in digital camera, IEEE Signal Processing Letters 16(2): 133-136.
• Li, X., He, M. and Roux, M. (2010). Multifocus image fusion based on redundant wavelet transform, IET Image Processing 4(4): 283-293.
• Lim, J.S. (1990). Two Dimensional Signal and Image Processing, Prentice Hall, Upper Saddle River, NJ.
• Macmillan, N. and Creelman, C. (Eds) (2005). Detection Theory: A User's Guide, Lawrence Erlbaum, Mahwah, NJ.
• Nayar, S. and Nakagawa, Y. (1994). Shape from focus, IEEE Transactions on Pattern Analysis and Machine Intelligence 16(8): 824-831.
• Oppenheim, A.V. (1965). Superposition in a class of non-linear system, Technical report, MIT, Cambridge, MA.
• Oppenheim, A.V. (1967). Generalized superposition, Information and Control 11(5,6): 528-536.
• Panetta, K., Wharton, E. and Agaian, S. (2008). Human visual system-based image enhancement and logarithmic contrast measure, IEEE Transactions on Systems, Man, and Cybernetics, B: Cybernetics 38(1): 174-188.
• Panetta, K., Zhou, Y., Agaian, S. and Wharton, E. (2011). Parameterized logarithmic framework for image enhancement, IEEE Transactions on Systems, Man, and Cybernetics, B: Cybernetics 41(2): 460-472.
• Pinoli, J.C. and Debayle, J. (2007). Logarithmic adaptive neighborhood image processing (LANIP): Introduction, connections to human brightness perception, and application issues, EURASIP Journal on Advances in Signal Processing 036105(1), Article ID 36105, DOI: 10.1155/2007/36105.
• Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M. and Battisti, F. (2009). A database for evaluation of full-reference visual quality assessment metrics, Advances of Modern Radioelectronics 10(1): 30-45.
• Pătraşcu, V. and Voicu, I. (2000). An algebraical model for gray level images, Proceedings of the Exhibition on Optimization of Electrical and Electronic Equipment, OPTIM, Brasov, Romania, pp. 809-812.
• Ramanath, R., Snyder, W., Yoo, Y. and Drew, M. (2005). Color image processing pipeline: A general survey of digital still camera processing, IEEE Signal Processing Magazine 22(1): 34-43.
• Russell, S.J. and Norvig, P. (2003). Artificial Intelligence: A Modern Approach, Prentice Hall, Upper Saddle River, NJ.
• Stevens, J. and Stevens, S. (1963). Brightness functions: Effects of adaptation, Journal of the Optical Society of America 53(3): 375-385.
• Stevens, S. (1961). To honor Fechner and repeal his law, Science 133(3446): 80-133.
• Subbarao, M. and Tyan, J. (1998). Selecting the optimal focus measure for autofocussing and depth-from-focus, IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8): 864-870.
• Sun, Y., Duthaler, S. and Nelson, B. (2005). Autofocusing algorithm selection in computer microscopy, Proceedings of the International Conference on Intelligent Robots and Systems, Edmonton, Canada, pp. 809-812.
• Svahn, F. (1996). Tools and Methods to Obtain a Passive Autofocus System, Master's thesis, Technical University of Linkoping, Linkoping, www.viktoria.se/˜fresva/documents/master_thesis.pdf.
• Vertan, C., Oprea, A., Florea, C. and Florea, L. (2008). A pseudo-logarithmic framework for edge detection, in J.B. Talon, S. Bourennane, W. Philips, D. Popescu and P. Scheunders (Eds.), Advances in Computer Vision, Lecture Notes in Computer Science, Vol. 5259, Springer-Verlag, Juan-les-Pins, pp. 637-644.
• Vollath, D. (1987). Automatic focusing by correlative methods, Journal of Microscopy 147(3): 279-288.
• Wu, Q.Z. and Jeng, B.S. (2002). Background subtraction based on logarithmic intensities, Pattern Recognition Letters 23(13): 1529-1536.

Identyfikatory

DOI

10.2478/amcs-2013-0048