Brightness preserving weight clustering histogram equalization

Sengee, Nyamlkhagva; Choi, Heung‐Kook

doi:10.1109/tce.2008.4637624

Cited by 76 publications

(29 citation statements)

References 9 publications

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“…Histogram Equalization (HE) is a very popular technique for contrast enhancement of images (Kim and Paik, 2008;Sengee and Choi, 2008). Contrast of images is determined by its dynamic range, which is defined as the ratio between the brightest and the darkest Apixel intensities.…”

Section: Methodsmentioning

confidence: 99%

“…In order to overcome the limitations of HE, several brightness preserving methods have been proposed (Kim et al, 2001;Chen and Ramli, 2003a;Sun et al, 2005;Chen et al, 2006;Wang and Ward, 2007;Sengee and Choi, 2008;Wang and Ye, 2005;Agaian et al, 2007). One of the popular brightness preserving methods is the mean Brightness preserving BiHistogram Equalization (BBHE) introduced by Kim (Chen and Ramli, 2003b).…”

Section: Brightness Preserving Bi-histogram Equalizationmentioning

confidence: 99%

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An Efficient Method for Contrast Enhancement in Still Images using Histogram Modification Framework

Ravichandran¹

2012

Journal of Computer Science

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Problem statement: Image enhancement improves an image appearance by increasing dominance of some features or by decreasing ambiguity between different regions of the image. Histogram based image enhancement technique is mainly based on equalizing the histogram of the image and increasing the dynamic range corresponding to the image. Approach: Histogram Equalization is widely used in different ways to perform contrast enhancement in images. As a result, such image creates side-effects such as washed out appearance and false contouring due to the significant change in brightness. In order to overcome these problems, mean brightness preserving Histogram Equalization based techniques have been proposed. Generally, these methods partition the histogram of the original image into sub histograms and then independently equalize each sub histogram with Histogram Equalization. Results: The comparison of recent histogram based techniques is presented for contrast enhancement in low illumination environment and the experiment results are collected using low light environment images. Conclusion: The histogram modification algorithm is simple and computationally effective that makes it easy to implement and use in real time systems.

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Section: Methodsmentioning

confidence: 99%

Section: Brightness Preserving Bi-histogram Equalizationmentioning

confidence: 99%

An Efficient Method for Contrast Enhancement in Still Images using Histogram Modification Framework

Ravichandran¹

2012

Journal of Computer Science

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“…The best well known strategy for differentiation upgrade of pictures is histogram balance (HE) [7][8][9][10][11]. It is one of the decently kenned techniques for upgrading the complexity of a given picture as per the examples circulation [12,13]. HE is a basic and effective complexity improvement strategy which conveys pixel values consistently such that upgraded picture have direct aggregate histograms.…”

Section: Histogram Equalizationmentioning

confidence: 99%

“…Of course, the DHE rejects the mean iridescence sparing and slants to drive submersion doodads. To surmount the drawback of the DHE, Brightness Preserving Dynamic Histogram Equalization (BPDHE) have been proposed by Ibrahim and Kong [29] in 2007, an incremental strategy for the DHE and Multi Peak Histogram Equalization with Brightness Preserving (MPHEBP) and parcels the data histogram predicated on adjacent most noteworthy quality. BPDHE shows more predominant contrast overhaul stood out from MPHEBP and mean quality, ensuring appeared differently in relation to DHE.…”

Section: Figure 4 Recursive Mean-separate Histogram Equalization (Rmsmentioning

confidence: 99%

Histogram Equalization Tool: Brightness Preservation and Contrast Enhancement using Segmentation with Opening-by-Reconstruction

Kaur¹

2015

IJCA

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Sundry improvement plans are used for improving a picture which incorporates ash scale control, sifting and Histogram Equalization (HE). The issue with pictures is that, their quality depends upon a number of different variables like lighting in the picture catching area, commotion and capability of the administrator. The writing addresses the verbalized issue widely and presents answers for them. Contrast improvement systems are used for correcting visual nature of low difference pictures. Histogram Equalization (HE) is one such procedure used for difference upgrade. The proposed illustrations have a few shared traits in their procedures. Approximately every one of them is at fluctuation either in histogram leveling strategies or in picture quality estimation instruments. An instrument is lost in the writing that is proficient to improve the picture and even perform the examination. In this paper, a GUI apparatus is composed which is coupled with different procedures of picture improvement through histogram balance. Opening-byrecreation is a standout amongst the most effective picture division strategy is used to attain to the craved results. To assess the adequacy of the delineated systems; PSNR, Tenengrad, and Absolute Mean Brightness mistake (AMBE) are used as parameters. The results are decently backed by the parameter estimations toward the end. General TermsDigital Image Processing, Image Enhancement and Histogram Equalization.

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“…MMBEBHE is the extension of BBHE method that provides maximal brightness preservation. Though these methods can perform good contrast enhancement, they also cause more annoying side effects depending on the variation of gray level distribution in the histogram [17]. Recursive Mean-Separate Histogram Equalization (RMSHE) [5] is another improvement of BBHE.…”

Section: Introductionmentioning

confidence: 99%