Contrast enhancement of images using Partitioned Iterated Function Systems

  • Authors:

  • T. L. Economopoulos;P. A. Asvestas;G. K. Matsopoulos

  • Affiliations:

  • National Technical University of Athens, School of Electrical and Computer Engineering, 9, Iroon Polytechniou Str, Zografos, 15780, Athens, Greece;National Technical University of Athens, School of Electrical and Computer Engineering, 9, Iroon Polytechniou Str, Zografos, 15780, Athens, Greece;National Technical University of Athens, School of Electrical and Computer Engineering, 9, Iroon Polytechniou Str, Zografos, 15780, Athens, Greece

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2010

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Abstract

A new algorithm for the contrast enhancement of images, based on the theory of Partitioned Iterated Function System (PIFS), is presented. A PIFS consists of contractive transformations, such that the original image is the fixed point of the union of these transformations. Each transformation involves the contractive affine spatial transform of a square block, as well as the linear transform of the gray levels of its pixels. The transformation of the gray levels is determined by two parameters which adjust the brightness and the contrast of the transformed block. The PIFS is used in order to create a lowpass version of the original image. The contrast-enhanced image is obtained by adding the difference of the original image with its lowpass version, to the original image itself. The proposed algorithm uses a predefined constant value for the contrast parameter, whereas, the parameters of the affine spatial transform, as well as the parameter adjusting the brightness, are calculated using k-dimensional trees. The lowpass version of the original image is obtained applying the PIFS on the original image repeatedly while using a value for the contrast parameter that is lower than the predefined one. Quantitative and qualitative results stress the superior performance of the proposed contrast enhancement algorithm against four other widely used contrast enhancement methods; namely, linear and nonlinear unsharp masking, Contrast Limited Adaptive Histogram Equalization and Local Range Modification.