An automatic crossover point selection technique for image enhancement using fuzzy sets

Fang, Ning; Cheng, Ming-Chieh

doi:10.1016/0167-8655(93)90118-w

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…However, all these research works share the same goal of retrieving impo rtant details while processing degraded images. In [16] the authors used fuzzy intensification operators for the purpose of image enhancement, which is to improve the recognition of grayscale optical characters, while the authors of [17] used a modified version of such operators with an automatic crossover point selection to imp rove the contrast of dig ital images. In addition, the authors of [18] emp loyed the aforementioned operators to reduce the computation cost of their algorith m and thus provide a better image segmentation process.…”

Section: Literature Reviewmentioning

confidence: 99%

Visibility Enhancement for Images Captured in Dusty Weather via Tuned Tri-threshold Fuzzy Intensification Operators

Al-Ameen¹

2016

IJISA

136

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An inclement dusty weather can significantly reduce the visual quality of captured images, wh ich consequently hampers the observation of important image details. Capturing images in such weather often yields undesirable artifacts such as poor contrast, deficient colors or color cast. Hence, various methods have been proposed to process such unwanted events and recover lucid results with acceptable colors. These methods vary fro m simple to co mplex due to the variat ion of the used processing concepts. In this article, an innovative technique that utilizes tuned fuzzy intensification operators is introduced to expeditiously process poor quality images captured in an inclement dusty weather. Intensive experiments were carried out to check the processing ability of the proposed technique, wherein the obtained results exhib ited its competence in filtering various degraded images. Specifically, it perfo rmed well in provid ing acceptable colors and unveiling fine details for the processed images.

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Section: Literature Reviewmentioning

confidence: 99%

Visibility Enhancement for Images Captured in Dusty Weather via Tuned Tri-threshold Fuzzy Intensification Operators

Al-Ameen¹

2016

IJISA

136

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“…-classical filtering (e.g. Gaussian [28]), PDE filtering [77,78,79] the simplest being the heat operator analog for the Gaussian filter, grey histogram thresholding [26,30], entropy techniques [25], adaptive filtering [64] -multi-scale, in particular wavelets [84]) approach [27,31] -fuzzy clustering [29] -specific hardware implementation [60,62] for real-time procedures [22], using either the simplest PC based [23] or the most sophisticated architectures (SIMD [32,52] or MIMD [68])…”

Section: Discrete Operatorsmentioning

confidence: 99%

“…In order to examine how rod and cone functions are differentially affected during retinal degeneration (abolishing the contrast), many studies have been done on the genetic level showing that these two cell types have complementary roles during both development and degenerative processes [19][20][21]. For understanding the retinal physiology as well as this pathology, many models [22][23][24][25][26][27][28][29][30][31][32][33][34] are now available which try to mimic relevant adaptation behaviours of the human visual system, like lightness/colour constancy and contrast enhancement, corresponding to the ability of the visual system to increase the appearance of large-scale light-dark or inter-colour transitions, similar to how sharpening with an ''un-sharp mask'' increases the appearance of small-scale edges.…”

Section: Introductionmentioning

confidence: 99%

Correction: Understanding Physiological and Degenerative Natural Vision Mechanisms to Define Contrast and Contour Operators

Demongeot¹,

Fouquet²,

Tayyab³

et al. 2009

PLoS ONE

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Background: Dynamical systems like neural networks based on lateral inhibition have a large field of applications in image processing, robotics and morphogenesis modeling. In this paper, we will propose some examples of dynamical flows used in image contrasting and contouring.Methodology: First we present the physiological basis of the retina function by showing the role of the lateral inhibition in the optical illusions and pathologic processes generation. Then, based on these biological considerations about the real vision mechanisms, we study an enhancement method for contrasting medical images, using either a discrete neural network approach, or its continuous version, i.e. a non-isotropic diffusion reaction partial differential system. Following this, we introduce other continuous operators based on similar biomimetic approaches: a chemotactic contrasting method, a viability contouring algorithm and an attentional focus operator. Then, we introduce the new notion of mixed potential Hamiltonian flows; we compare it with the watershed method and we use it for contouring. Conclusions:We conclude by showing the utility of these biomimetic methods with some examples of application in medical imaging and computed assisted surgery.

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Fuzzy Image Enhancement: An Overview

Tizhoosh¹

2000

Fuzzy Techniques in Image Processing

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An automatic crossover point selection technique for image enhancement using fuzzy sets

Cited by 7 publications

References 4 publications

Visibility Enhancement for Images Captured in Dusty Weather via Tuned Tri-threshold Fuzzy Intensification Operators

Visibility Enhancement for Images Captured in Dusty Weather via Tuned Tri-threshold Fuzzy Intensification Operators

Correction: Understanding Physiological and Degenerative Natural Vision Mechanisms to Define Contrast and Contour Operators

Fuzzy Image Enhancement: An Overview

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