Open Access

Adaptive Inverse Hyperbolic Tangent Algorithm for Dynamic Contrast Adjustment in Displaying Scenes

  • Cheng-Yi Yu1, 2,
  • Yen-Chieh Ouyang1Email author,
  • Chuin-Mu Wang2 and
  • Chein-I Chang3
EURASIP Journal on Advances in Signal Processing20102010:485151

DOI: 10.1155/2010/485151

Received: 16 November 2009

Accepted: 11 February 2010

Published: 3 May 2010


Contrast has a great influence on the quality of an image in human visual perception. A poorly illuminated environment can significantly affect the contrast ratio, producing an unexpected image. This paper proposes an Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm to improve the display quality and contrast of a scene. Because digital cameras must maintain the shadow in a middle range of luminance that includes a main object such as a face, a gamma function is generally used for this purpose. However, this function has a severe weakness in that it decreases highlight contrast. To mitigate this problem, contrast enhancement algorithms have been designed to adjust contrast to tune human visual perception. The proposed AIHT determines the contrast levels of an original image as well as parameter space for different contrast types so that not only the original histogram shape features can be preserved, but also the contrast can be enhanced effectively. Experimental results show that the proposed algorithm is capable of enhancing the global contrast of the original image adaptively while extruding the details of objects simultaneously.

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Authors’ Affiliations

Department of Electrical Engineering, National Chung Hsing University
Department of Computer Science and Information Engineering, National Chin Yi University of Technology
Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County


© Cheng-Yi Yu et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.