Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement

Ibrahim, Haidi; Kong, N.S.P.

doi:10.1109/tce.2007.4429280

Cited by 835 publications

(392 citation statements)

References 9 publications

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“…Using this concept, Ramaraj et al [4], proposed a homomorphic filtering technique. By applying frequency domain transformation to the input image using DFT and appropriate design of Butterworth filter they claimed to obtain an enhancement result compared to contrast limited adaptive histogram equalization (CLAHE) [5]. More recently, Okuhata et al [6] applied retinex theory to the problem of CVE image enhancement [7].…”

Section: Introductionmentioning

confidence: 99%

“…Attar et al [14] combined histogram equalization with an edge preserving process to enhance WCE images. Other similar works include [5,15,16]. Among recent computational methods that are proposed for capsule endoscopy image enhancement include Rukundo et al [17], where the authors proposed an algorithm that uses a half-unit weighted-bilinear filter for darker areas and threshold weighted-bilinear method to avoid overexposure and enlargement of specular highlight spots while preserving the hue.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Capsule Endoscopy Image Enhancement

et al. 2018

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Abstract:Capsule endoscopy, which uses a wireless camera to take images of the digestive tract, is emerging as an alternative to traditional colonoscopy. The diagnostic values of these images depend on the quality of revealed underlying tissue surfaces. In this paper, we consider the problem of enhancing the visibility of detail and shadowed tissue surfaces for capsule endoscopy images. Using concentric circles at each pixel for random walks combined with stochastic sampling, the proposed method enhances the details of vessel and tissue surfaces. The framework decomposes the image into two detailed layers that contain shadowed tissue surfaces and detail features. The target pixel value is recalculated for the smooth layer using similarity of the target pixel to neighboring pixels by weighting against the total gradient variation and intensity differences. In order to evaluate the diagnostic image quality of the proposed method, we used clinical subjective evaluation with a rank order on selected KID image database and compared it to state-of-the-art enhancement methods. The result showed that the proposed method provides a better result in terms of diagnostic image quality and objective quality contrast metrics and structural similarity index.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic Capsule Endoscopy Image Enhancement

et al. 2018

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“…Originally, Isaac Newton had described the decomposition of sunlight into the colour spectrum when passing it through a glass prism in 1666. [3].…”

Section: Introductionmentioning

confidence: 99%

Contrast enhancement of colour digital images

et al. 2017

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Preliminary communicationThe main parts of digital images processing are procedures that improve the images quality which does not improve information content, but ensures that the image is visually better and more suitable for display. That is achieved through improved contrast, noise removing, sharpening and colouring pictures. Contrast is the rate of frequency changes in illumination and recording the difference between light and dark elements of the image in the brightness level. Contrast is the difference between two adjacent surfaces' brightness. Absolute value of the brightness of an object is of less importance than the relative brightness (contrast). Objects that have the same objective value of luminance may cause differences in the subjective sense of brightness depending on the contrast. This paper presents the improvement of the digital images quality in colour, which are the result of volcanic activity recording in the infrared region of the electromagnetic energy spectrum, with the application process to improve the contrast of images. High-quality digital images of the volcano were treated in high spatial resolution, which enabled the successful application of MATLAB and the results were very clear, precise and expressive images. Keywords: contrast; digital image; improving the quality; MATLAB; Plosky Tolbachik; volcano Poboljšanje kontrasta digitalnih snimaka u bojiPrethodno priopćenje Osnovna područja obrade digitalnih snimaka su postupci poboljšanja kvalitete snimaka, čime se ne popravlja informacijski sadržaj, već se postiže da slika bude vizualno kvalitetnija i pogodnija za prikaz, što se ostvaruje kroz poboljšanje kontrasta, otklanjanje šuma, izoštravanje i bojanje slike. Kontrast je mjera učestalosti promjene osvjetljenja na snimku i predstavlja razliku između svijetlih i tamnih elemenata snimka u nivou osvijetljenosti. Kontrast predstavlja razliku svjetline dviju susjednih površina. Apsolutna vrijednost svjetline nekog objekta je od manjeg značaja od relativne svjetline (kontrasta). Objekti koji imaju istu objektivnu vrijednost svjetline (luminance), mogu izazvati različit subjektivni osjećaj svjetline (brightness) zavisno od kontrasta. U ovom radu će biti predstavljeno poboljšanje kvaliteta digitalnih snimaka u boji, koji su rezultat snimanja vulkanske aktivnosti u infracrvenom području spektra elektromagnetne energije, uz primjenu postupka poboljšanja kontrasta snimaka. Obrađivani su kvalitetni digitalni snimci vulkana visoke prostorne rezolucije, što je omogućilo uspješnu primjenu programskog paketa MATLAB i dobiveni su vrlo jasni, precizni i izražajni slikovni prikazi.

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“…It is claimed that DSIHE is better than BBHE in term of brightness preserving. The input histogram divided based on its shape was realized [3], [4]. Yang et al proposed a simple method named bin underflow and bin overflow (BUBO) [5].…”

Section: Introductionmentioning

confidence: 99%

Contrast Enhancement of Mycobacterium Tuberculosis Images Based on Improved Histogram Equalization

Zhou

Peng

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYThere are often low contrast Mycobacterium tuberculosis (MTB) objects in the MTB images. Based on improved histogram equalization (HE), a framework of contrast enhancement is proposed to increase the contrast of MTB images. Our proposed algorithm was compared with the traditional HE and the weighted thresholded HE. The experimental results demonstrate that our proposed algorithm has better performance in contrast enhancement, artifacts suppression, and brightness preserving for MTB images.

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Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement

Cited by 835 publications

References 9 publications

Stochastic Capsule Endoscopy Image Enhancement

Stochastic Capsule Endoscopy Image Enhancement

Contrast enhancement of colour digital images

Contrast Enhancement of Mycobacterium Tuberculosis Images Based on Improved Histogram Equalization

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