Adaptive contrast enhancement methods with brightness preserving

Ooi, Chen Hee; Isa, Nor Ashidi Mat

doi:10.1109/tce.2010.5681139

Cited by 150 publications

(73 citation statements)

References 12 publications

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“…It differs from Histogram Equalization by adaptive method that computes several histograms and each histogram corresponding to a distinct section of an image. An image, contrast region will not adequately enhanced by Histogram Equalization [15]. …”

Section: Adaptive Histogram Equalization (Ahe)mentioning

confidence: 99%

A Survey on Spatial Domain Image Enhancement Techniques

Rupa¹

2018

IJRASET

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Image Enhancement is one of the challenging issues in computer vision and image processing. The major goal of image enhancement is to increase the quality of image with respect to contrast and visual appearance or structural appearance without any degradation in the input image. The enhancement techniques mainly used for the identification of important features more easier by eliminating noise and other artifacts in an image. There are many image enhancement techniques existed which are mainly based on spatial and frequency domains. The spatial domain enhancement techniques basically deal with image intensity values or pixels and in second domain an image is transformed into frequency domain using fourier transforms, etc. This paper mainly explores various special domain image enhancement techniques which provides better contrast, visual appearance and further leads to improvement of image quality. Especially histogram equalization is a prominent enhancement method which is used for contrast adjustment using histograms so that quality of image has been improved.

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Section: Adaptive Histogram Equalization (Ahe)mentioning

confidence: 99%

A Survey on Spatial Domain Image Enhancement Techniques

Rupa¹

2018

IJRASET

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“…Brightness preservation methods are popular to the shopper electronic items. The histogram equalization (HE) based luminosity protecting techniques grade to induce undesirable relics in [6].…”

Section: Figure 1 Image Enhancementmentioning

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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“…This approach is widely used to measure the amount of details contained in images [2,14,18]. The entropy, E, of an image can be calculated using Eq.…”

Section: Quantitative Analysismentioning

confidence: 99%

“…∑∑ ∑∑ (14) where W and H are the width and height of the image respectively, g(u,v) is the intensity of the pixel at a 2-dimensional position (u,v).…”

Section: Quantitative Analysismentioning

confidence: 99%

An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

Tang

Isa

2014

J. ICT.Res.Appl.

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Abstract. This paper modifies the Adaptive Contrast Enhancement Algorithm with Details Preserving (ACEDP) technique by integrating a fuzzy element in the image type selection. The proposed technique, named the Adaptive Fuzzy Contrast Enhancement with Details Preserving (AFCEDP) technique, first computes the degree of membership of the input image to three categories, i.e. low-, middle-or high-level images. The AFCEDP technique then clips the histogram at different plateau limits that are computed from both the degree of membership and the clipping functions. The classification of an image in the ACEDP technique is done based solely on the intensity range of the maximum number of pixels, which may be inaccurate. In the proposed AFCEDP technique, the image type classification is handled in a better way with the integration of a fuzzy element. The performance of the proposed AFCEDP technique was compared with the conventional ACEDP technique and several state-of-art techniques described in the literature. The simulation results revealed that the AFCEDP technique demonstrates good capability in contrast enhancement and detail preservation. In addition, the experiments using cervical cell images and HEp-2 cell images showed great potential of the AFCEDP technique as a technique for enhancing medical microscopic images.

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Adaptive contrast enhancement methods with brightness preserving

Cited by 150 publications

References 12 publications

A Survey on Spatial Domain Image Enhancement Techniques

A Survey on Spatial Domain Image Enhancement Techniques

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

An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

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