2016
DOI: 10.5201/ipol.2016.140
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Implementation of the Midway Image Equalization

Abstract: In this paper, we present the detailed algorithm of the Midway Image Equalization giving to a pair of images the same histogram while maintaining as much as possible their previous gray dynamics. The midway equalization is primarily designed for gray level images, but can be applied channel-wise to color images. This method is easy to implement, fast to compute, fully automatic and requires no parameter. Source CodeThe source code and an online demo are accessible at the IPOL web page of this article 1 . The d… Show more

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Cited by 12 publications
(14 citation statements)
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“…The difference image DI defined in (8) requires the values I 2 (Ψ(x; p j−1 )), which are computed by bicubic interpolation [7]. Neumann boundary conditions are adopted for both, the gradient estimation, and the interpolation.…”
Section: Algorithmmentioning
confidence: 99%
See 1 more Smart Citation
“…The difference image DI defined in (8) requires the values I 2 (Ψ(x; p j−1 )), which are computed by bicubic interpolation [7]. Neumann boundary conditions are adopted for both, the gradient estimation, and the interpolation.…”
Section: Algorithmmentioning
confidence: 99%
“…Note that contrast change may deteriorate the algorithm performance since it is not taken into account in (1). It can be handled by equalizing the input image contrasts, for instance using the Midway Image Equalization algorithm [8].…”
Section: Algorithmmentioning
confidence: 99%
“…In this work, we extend the implementation proposed in [8] to a sequence of images, following the method proposed in [4]. The implementation is similar to [8], with the histogram of the frames modified according to the information of several images in a temporal window.…”
Section: Introductionmentioning
confidence: 99%
“…The implementation is similar to [8], with the histogram of the frames modified according to the information of several images in a temporal window. We describe two implementations: the sorting method [16], based on the sorting of the images intensities; and the histogram method, based on the matching of normalized cumulative histograms.…”
Section: Introductionmentioning
confidence: 99%
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