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

Corneliu Florea; Laura Florea

International Journal of Applied Mathematics and Computer Science (2013)

  • Volume: 23, Issue: 3, page 637-648
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Corneliu Florea, and Laura Florea. "Parametric logarithmic type image processing for contrast based auto-focus in extreme lighting conditions." International Journal of Applied Mathematics and Computer Science 23.3 (2013): 637-648. <http://eudml.org/doc/262301>.

@article{CorneliuFlorea2013,
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.},
author = {Corneliu Florea, Laura Florea},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {logarithmic image processing; digital camera; auto-focus},
language = {eng},
number = {3},
pages = {637-648},
title = {Parametric logarithmic type image processing for contrast based auto-focus in extreme lighting conditions},
url = {http://eudml.org/doc/262301},
volume = {23},
year = {2013},
}

TY - JOUR
AU - Corneliu Florea
AU - Laura Florea
TI - Parametric logarithmic type image processing for contrast based auto-focus in extreme lighting conditions
JO - International Journal of Applied Mathematics and Computer Science
PY - 2013
VL - 23
IS - 3
SP - 637
EP - 648
AB - 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.
LA - eng
KW - logarithmic image processing; digital camera; auto-focus
UR - http://eudml.org/doc/262301
ER -

References

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