General Logarithmic Image Processing Convolution

The logarithmic image processing model (LIP) is a robust mathematical framework, which, among other benefits, behaves invariantly to illumination changes. This paper presents, for the first time, two general formulations of the 2-D convolution of separable kernels under the LIP paradigm. Although both formulations are mathematically equivalent, one of them has been designed avoiding the operations which are computationally expensive in current computers. Therefore, this fast LIP convolution method allows to obtain significant speedups and is more adequate for real-time processing. In order to support these statements, some experimental results are shown in Section V

[1]  Pierre Vandergheynst,et al.  Multiresolution segmentation of natural images: from linear to nonlinear scale-space representations , 2004, IEEE Transactions on Image Processing.

[2]  Vittoria Bruni,et al.  A generalized model for scratch detection , 2004, IEEE Transactions on Image Processing.

[3]  Jimin Liang,et al.  Improving the detection of low-density weapons in x-ray luggage scans using image enhancement and novel scene-decluttering techniques , 2004, J. Electronic Imaging.

[4]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[5]  Giovanni Ramponi,et al.  A cubic unsharp masking technique for contrast enhancement , 1998, Signal Process..

[6]  Vasile Patrascu,et al.  Color Image Enhancement Using the Support Fuzzification , 2003, IFSA.

[7]  Jerry D. Gibson,et al.  Handbook of Image and Video Processing , 2000 .

[8]  D. Baylor,et al.  Responses of retinal rods to single photons. , 1979, The Journal of physiology.

[9]  Eric L. Miller,et al.  Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images , 2004, IEEE Transactions on Image Processing.

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

[11]  Til Aach,et al.  Illumination-invariant change detection , 2000, 4th IEEE Southwest Symposium on Image Analysis and Interpretation.

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

[13]  Vasile Buzuloiu,et al.  Image dynamic range enhancement in the context of logarithmic models , 2002, 2002 11th European Signal Processing Conference.

[14]  I. Gordon.  Theories of Visual Perception , 1989 .

[15]  D. Jameson,et al.  The perception of brightness and darkness , 1970 .

[16]  Michael Potmesil,et al.  Modeling motion blur in computer-generated images , 1983, SIGGRAPH.

[17]  Guang Deng,et al.  Differentiation-Based Edge Detection Using the Logarithmic Image Processing Model , 1998, Journal of Mathematical Imaging and Vision.

[18]  Jean-Charles Pinoli,et al.  A general comparative study of the multiplicative homomorphic, log-ratio and logarithmic image processing approaches , 1997, Signal Process..

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

[20]  Irfan A. Essa,et al.  Image-based motion blur for stop motion animation , 2001, SIGGRAPH.

[21]  Franck Luthon,et al.  Nonlinear color space and spatiotemporal MRF for hierarchical segmentation of face features in video , 2004, IEEE Transactions on Image Processing.

[22]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[23]  A. Oppenheim,et al.  Nonlinear filtering of multiplied and convolved signals , 1968 .

[24]  Andrew P. Witkin,et al.  Scale-space filtering: A new approach to multi-scale description , 1984, ICASSP.

[25]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[26]  W. Rudin.  Real and complex analysis , 1968 .

[27]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[28]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.