Fast and reliable image enhancement using fuzzy relaxation technique

Li, H.; Yang, Hyun Seung

doi:10.1007/3-540-19036-8_58

Cited by 43 publications

(20 citation statements)

References 8 publications

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“…The result of applying these measures to the three face images is tabulated in Tables 1{3. For convenience, the shape and uniformity measures are repeated here: Uniformity = 1 ; P x y R (f(x y) ; ) 2 B (27) where R is the thresholded region of concern, f returns the gray-level, is the mean gray level within the region and B is a normalizing factor based on the region area and its graylevel range. Uniformity is a least-squares measure of the departure of each region selected by a putative threshold from its mean gray-level value.…”

Section: Resultsmentioning

confidence: 99%

Candidate Functions for a Parallel Multi-level Thresholding Technique

Hayat

Fleury

Clark

1996

Graphical Models and Image Processing

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Section: Resultsmentioning

confidence: 99%

Candidate Functions for a Parallel Multi-level Thresholding Technique

Hayat

Fleury

Clark

1996

Graphical Models and Image Processing

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“…In this paper, we use the S-function to represent the brightness of gray level. It is defined as [12] Fuzzy entropy-based segmentation method always regard the maximum fuzzy entropy as the threshold selecting principle. The shape of S-function is determined by parameter a, b and d. Therefore, the threshold selecting problem becomes to find a combination of the parameters such that the corresponding event has the maximum fuzzy entropy [2,13].…”

Section: Generalized Fuzzy Entropy-based Image Segmentation Methodsmentioning

confidence: 99%

A generalized fuzzy entropy-based image segmentation method

Fan¹,

Zhao²

2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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Image segmentation using fuzzy entropy is an important and common segmentation method. The threshold of fuzzy entropy is mostly selected at the gray value with fuzzy membership degree 0.5. It is a limitation in some cases. In order to solve this problem, we present a new definition of generalized fuzzy entropy and apply it to image segmentation. Compared with the traditional fuzzy entropy-based image segmentation method, the proposed method segments an image using the threshold with membership degree m (0

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“…By tuning this single parameter, user can achieve the desired enhanced look of the intended objects in an image is developed. [14] have demonstrated an efficient way of contrast enhancement based on the fuzzy relaxation technique with improved speed and quality. Different orders of fuzzy mem-bership functions were tried out by various researchers in order to improve the speed and quality of contrast enhancement based on the fuzzy logic method.…”

Section: Literature Surveymentioning

confidence: 99%

Contrast Enhancement of an Image using Fuzzy Logic

Sharma¹,

Bhatia²

2015

IJCA

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Image enhancement plays a significant role in vision applications. Many techniques have been proposed so far for enhancing the images. It has been found that the most of the existing techniques are based upon the transform domain methods; which may introduce the color artefacts and also may reduce the intensity of the input remote sensing image. To overcome this problem a modified approach is introduced in this research work. The new integrated approach has the capability to enhance the contrast in digital images in efficient manner by using the modified fuzzy based enhancement algorithm. Modified fuzzy image enhancement has integrated image gradients with input image for image enhancement. After image enhancement using modified fuzzy based algorithms the color normalization has come in action to reduce color artefacts. In order to evaluate the significant improvement of the proposed various well known images has been selected for experimental results. The experimental results have shown that the proposed technique has quite effective improvement over the available techniques.

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Fast and reliable image enhancement using fuzzy relaxation technique

Cited by 43 publications

References 8 publications

Candidate Functions for a Parallel Multi-level Thresholding Technique

Candidate Functions for a Parallel Multi-level Thresholding Technique

A generalized fuzzy entropy-based image segmentation method

Contrast Enhancement of an Image using Fuzzy Logic

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