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

Abstract 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.

[1]  M. Roux,et al.  Multifocus image fusion based on redundant wavelet transform , 2010 .

[2]  C. Vertan,et al.  Non-linear parametric derivation of contour detectors for cellular images , 2009, 2009 International Symposium on Signals, Circuits and Systems.

[3]  Eric Krotkov,et al.  Focusing , 2004, International Journal of Computer Vision.

[4]  Jean-Charles Pinoli,et al.  Image dynamic range enhancement and stabilization in the context of the logarithmic image processing model , 1995, Signal Process..

[5]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[6]  D. Vollath.  Automatic focusing by correlative methods , 1987 .

[7]  S. S. Stevens,et al.  Brightness function: effects of adaptation. , 1963, Journal of the Optical Society of America.

[8]  Donald P. Greenberg,et al.  A model of visual adaptation for realistic image synthesis , 1996, SIGGRAPH.

[9]  Sos S. Agaian,et al.  Parameterized Logarithmic Framework for Image Enhancement , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Anna Fabijanska,et al.  A survey of subpixel edge detection methods for images of heat-emitting metal specimens , 2012, Int. J. Appl. Math. Comput. Sci..

[11]  Eric C. Larson,et al.  Most apparent distortion: full-reference image quality assessment and the role of strategy , 2010, J. Electronic Imaging.

[12]  Bor-Shenn Jeng,et al.  Background subtraction based on logarithmic intensities , 2002, Pattern Recognit. Lett..

[13]  Muralidhara Subbarao,et al.  Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Neil A. Macmillan,et al.  Detection Theory: A User's Guide , 1991 .

[15]  Witold Byrski,et al.  The role of parameter constraints in EE and OE methods for optimal identification of continuous LTI models , 2012, Int. J. Appl. Math. Comput. Sci..

[16]  G. R. Tobin,et al.  The study of logarithmic image processing model and its application to image enhancement , 1995, IEEE Trans. Image Process..

[17]  Bradley J. Nelson,et al.  Autofocusing algorithm selection in computer microscopy , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Jorge Herbert de Lira,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[19]  Alan V. Oppenheim,et al.  Generalized Superposition , 1967, Information and Control.

[20]  Sos S. Agaian,et al.  Human Visual System-Based Image Enhancement and Logarithmic Contrast Measure , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  A. Oppenheim.  SUPERPOSITION IN A CLASS OF NONLINEAR SYSTEMS , 1965 .

[22]  Corneliu Florea,et al.  A parametric non-linear algorithm for contrast based auto-focus , 2011, 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing.

[23]  M Fernandes,et al.  Improving focus measurements using logarithmic image processing , 2011, Journal of microscopy.

[24]  M. Jourlin,et al.  Logarithmic Image Processing for Color Images , 2011 .

[25]  Shree K. Nayar,et al.  Shape from Focus , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Soo-Won Kim,et al.  Reduced Energy-Ratio Measure for Robust Autofocusing in Digital Camera , 2009, IEEE Signal Processing Letters.

[27]  Nikolay N. Ponomarenko,et al.  TID2008 – A database for evaluation of full-reference visual quality assessment metrics , 2004 .

[28]  W.E. Snyder,et al.  Color image processing pipeline , 2005, IEEE Signal Processing Magazine.

[29]  S. Stevens.  To Honor Fechner and Repeal His Law , 2008 .

[30]  Jean-Charles Pinoli,et al.  Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues , 2007, EURASIP J. Adv. Signal Process..

[31]  Jean-Charles Pinoli,et al.  A model for logarithmic image processing , 1988 .

[32]  G. Deng,et al.  An Entropy Interpretation of the Logarithmic Image Processing Model With Application to Contrast Enhancement , 2009, IEEE Trans. Image Process..

[33]  Matej Kristan,et al.  A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform , 2006, Pattern Recognit. Lett..

[34]  J. Michel,et al.  Logarithmic image processing: additive contrast, multiplicative contrast, and associated metrics , 2001 .

[35]  S S Stevens,et al.  To Honor Fechner and Repeal His Law: A power function, not a log function, describes the operating characteristic of a sensory system. , 1961, Science.

[36]  Guang Deng,et al.  A Generalized Logarithmic Image Processing Model Based on the Gigavision Sensor Model , 2012, IEEE Transactions on Image Processing.

[37]  Alina Oprea,et al.  A Pseudo-logarithmic Image Processing Framework for Edge Detection , 2008, ACIVS.

[38]  Vasile Patrascu.  An Algebraical Model for Gray Level Images , 2014, ArXiv.

[39]  Neil A. Macmillan,et al.  Detection theory: A user's guide, 2nd ed. , 2005 .