Image Enhancement with Noise Suppression Ability Using Histogram Manipulations

Ibrahim, Haidi; Kong, N.S.P.; Ooi, Chen Hee; Chieh, Derek Chan Juinn

doi:10.1109/icise.2009.647

Cited by 2 publications

(2 citation statements)

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“…These methods avoid the appearance of unwanted artifacts in their output images by controlling the enhancement rate, which relates to the slope of the intensity mapping function, by modifying the shape of the input histogram. Such modifications are normally been carried out by clipping the histogram [4], applying a weighting function to the histogram's bins [10], or creating a complementary histogram [11].…”

Section: Introductionmentioning

confidence: 99%

“…However, GHE and its derivative methods process the image globally, which mean that these methods use only one monotonically increasing transformation function for each input image. As mentioned in [11], the calculation for this discrete transformation function is done by using discrete values. Therefore, the contrast enhancement is obtained by increasing the spaces between consecutive histogram bins.…”

Section: Introductionmentioning

confidence: 99%

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Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms

Ibrahim

Hoo²

2014

Mathematical Problems in Engineering

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Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%