Contrast-limited adaptive histogram equalization: speed and effectiveness

Pizer, Stephen M.; Johnston, Richard; Ericksen, James P.; Yankaskas, Bonnie C.; Muller, Keith E.

doi:10.1109/vbc.1990.109340

Cited by 421 publications

(216 citation statements)

References 3 publications

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“…To solve this problem, the sigmoid function and locally adaptive histogram equalization is used to enhance the contrast of the estimated illumination component [16,22]. Finally, the resulting image is reconstructed by multiplying the enhanced illumination and estimated reflectance aŝ…”

Section: Optimal Reflectance and Illumination Components Estimationmentioning

confidence: 99%

Low-light image restoration using bright channel prior-based variational Retinex model

Park

Moon

et al. 2017

J Image Video Proc.

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This paper presents a low-light image restoration method based on the variational Retinex model using the bright channel prior (BCP) and total-variation minimization. The proposed method first estimates the bright channel to control the amount of brightness enhancement. Next, the variational Retinex-based energy function is iteratively minimized to estimate the improved illumination and reflectance using the BCP. Contrast of the estimated illumination is enhanced using the gamma correction and histogram equalization to reduce a color distortion and noise amplification. Experimental results show that the proposed method can provide the better restored result than the existing methods without unnatural artifacts such as noise amplification and halo effects near edges.

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Section: Optimal Reflectance and Illumination Components Estimationmentioning

confidence: 99%

Low-light image restoration using bright channel prior-based variational Retinex model

Park

Moon

et al. 2017

J Image Video Proc.

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“…Median filtering replaces the value of each pixel with median of the surrounding window. After filtering, the contrast of the image is enhanced through Contrast Limited Adaptive Histogram Equalisation (CLAHE) [18], so that the contrast between the vessels and the other structures are prominent making the blood vessel extraction effective. Then, Wavelet Transformation with symmetry filters is applied to extract the blood vessels.…”

Section: Image Processing Phasementioning

confidence: 99%

Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques

Ramani¹,

Sugirtharani²,

Lakshmi³

2017

IJCA

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Computational techniques are highly used in medical image analysis to aid the medical professionals. Glaucoma is a sight threatening retinal disease that needs attention at its early stages, though it does not reveal any symptoms. Glaucoma is identified usually through cup to disc ratio and ISNT rule. This work involves segmentation of blood vessels, segmentation of optic disc through proposed maximum voting of three segmentation algorithms (K-Means, Wavelet and Histogram based), segmentation of optic cup through intensity thresholding, feature extraction from these segmented structures, feature selection to identify siginificant features, hybrid model involving Naive Bayes to remove noise in data followed by ensemble classification of Reduced Error Pruning Tree. Optic disc segmentation methodology attains an average accuracy of 99.33%. Glaucoma detection accuracy reaches a maximum of 96.42%.

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“…Finally, every sub-image is equalized individually and then, these sub-images are combined together to form the complete image. All the aforementioned methods were developed to be used in many scientific applications except for CLAHE, which was developed to be used for medical applications [31,32]. The early application of CLAHE was on low-contrast CT medical images to improve their poor contrast, in which [32] clarified that it is possible to use this technique for clinical purposes.…”

Section: Related Workmentioning

confidence: 99%

An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization

Al-Ameen

Sulong

Rehman

et al. 2015

EURASIP J. Adv. Signal Process.

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Image contrast is an essential visual feature that determines whether an image is of good quality. In computed tomography (CT), captured images tend to be low contrast, which is a prevalent artifact that reduces the image quality and hampers the process of extracting its useful information. A common tactic to process such artifact is by using histogram-based techniques. However, although these techniques may improve the contrast for different grayscale imaging applications, the results are mostly unacceptable for CT images due to the presentation of various faults, noise amplification, excess brightness, and imperfect contrast. Therefore, an ameliorated version of the contrast-limited adaptive histogram equalization (CLAHE) is introduced in this article to provide a good brightness with decent contrast for CT images. The novel modification to the aforesaid technique is done by adding an initial phase of a normalized gamma correction function that helps in adjusting the gamma of the processed image to avoid the common errors of the basic CLAHE of the excess brightness and imperfect contrast it produces. The newly developed technique is tested with synthetic and real-degraded low-contrast CT images, in which it highly contributed in producing better quality results. Moreover, a low intricacy technique for contrast enhancement is proposed, and its performance is also exhibited against various versions of histogram-based enhancement technique using three advanced image quality assessment metrics of Universal Image Quality Index (UIQI), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). Finally, the proposed technique provided acceptable results with no visible artifacts and outperformed all the comparable techniques.

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Contrast-limited adaptive histogram equalization: speed and effectiveness

Cited by 421 publications

References 3 publications

Low-light image restoration using bright channel prior-based variational Retinex model

Low-light image restoration using bright channel prior-based variational Retinex model

Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques

An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization

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