Quantification of retinex in enhancement of weather degraded images

Joshi, Kalyani R.; Kamathe, Rupali

doi:10.1109/icalip.2008.4590120

Cited by 42 publications

(28 citation statements)

References 4 publications

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“…In our new glaucoma progression detection scheme presented in this study, we used the scale retinex approach. The retinex approach has been successfully utilized in many applications, including medical radiography [20], underwater photography [21] and weather images enhancement [22]. …”

Section: Glaucoma Progression Detection Schemementioning

confidence: 99%

A unified framework for glaucoma progression detection using Heidelberg Retina Tomograph images

Belghith

Balasubramanian

Bowd

et al. 2014

Computerized Medical Imaging and Graphics

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Glaucoma, the second leading cause of blindness worldwide, is an optic neuropthy characterized by distinctive changes in the optic nerve head (ONH) and visual field. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. In this context, ocular imaging equipment is increasingly sophisticated, providing quantitative tools to measure structural changes in ONH topography, an essential element in determining whether the disease is getting worse. In particular, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser technology, has been commonly used to detect glaucoma and monitor its progression. In this paper, we present a new framework for detection of glaucomatous progression using HRT images. In contrast to previous works that do not integrate a priori knowledge available in the images, particularly the spatial pixel dependency in the change detection map, the Markov Random Field is proposed to handle such dependency. To our knowledge, this is the first application of the Variational Expectation Maximization (VEM) algorithm for inferring topographic ONH changes in the glaucoma progression detection framework. Diagnostic performance of the proposed framework is compared to recently proposed methods of progression detection.

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Section: Glaucoma Progression Detection Schemementioning

confidence: 99%

A unified framework for glaucoma progression detection using Heidelberg Retina Tomograph images

Belghith

Balasubramanian

Bowd

et al. 2014

Computerized Medical Imaging and Graphics

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“…An effective approach proposed in [6][7][8] is adaptively enhancing the local image with local histogram equalization. Almost all the approaches mentioned above are compared and evaluated in [2,9]. But the defect of these methods above is that they neglect the physical property, such as the depth information of haze or the attenuation when light passing though the haze.…”

Section: Rd International Conference On Multimedia Technologyicmt 2013)mentioning

confidence: 99%

An Improved Single Image Haze Removal Algorithm Based on Dark Channel Prior and Histogram Specification

Zhu¹,

Shuai²,

Xie³

2013

Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)

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Abstract. We introduce an improved single image haze removal algorithm, which combines dark channel prior (DCP) and histogram specification. First, the dark channel prior knowledge proposed by Kaiming He is analyzed and a conclusion is drawn that the haze removal image based on dark channel prior will have a tendency to dim and indistinct in some specific situations. Especially, when cleaning the haze in the image with large background area and low contrast, DCP result appears obvious anamorphose. Next, in order to improve the dehazing result of this kind of image, we propose an approach to change the contrast and intensity of haze removal image after DCP method by rebuilding the histogram of the image. Then, a modified approach is applied to fit general haze image. We experiment our method with a variety of outdoor haze images. The effectiveness of our method is demonstrated in comparison with DCP result when the input image contains low contrast scene and large background area, such as thick fog or dark surroundings in dusk. Our job makes up the deficiency of the dark channel model for this kind of image and enhance the contrast of the scene. Furthermore, the experimental results show that the dehazing effect on general haze image appears more close to real scene than dark channel model.

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“…Theory of retinex has been introduced by Edward Land in 1986. Retinex algorithm is divided into three groups such as i) Single-scale retinex (SSR) ii) Multi-scale retinex (MSR) and iii) Multi-scale retinex with colour restoration (MSRCR) [43]. Retinex algorithm produced better results only for color images [50] however it cannot be a reason as a major drawback of the algorithm.…”

Section: A Non-model Based Methodsmentioning

confidence: 99%

“…The popular methods utilized model-based methods are histogram equalization [42], retinex algorithm [43,44] and wavelet based methods [45].…”

Section: A Non-model Based Methodsmentioning

confidence: 99%

Image contrast enhancement for outdoor machine vision applications

Wahab

Zakaria

Latip

et al. 2013

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)

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Abstract-Outdoor machine vision is getting a concern nowadays. Ranging from surveillance and monitoring system to automotive system such as driver assistance system require vision application or artificial eye to keep monitoring the situations. However, most of these applications works very well during clear weather and degrade during bad weather due to the atmospheric particles mitigate the quality of vision system. This paper discuss the state of the art of image enhancement techniques used to adjust the contrast of an outdoor image degrade by fog, haze, and rain. A brief overview of bad weather will be discussed and several recent techniques on removing fog, haze and rain are discussed.

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Quantification of retinex in enhancement of weather degraded images

Cited by 42 publications

References 4 publications

A unified framework for glaucoma progression detection using Heidelberg Retina Tomograph images

A unified framework for glaucoma progression detection using Heidelberg Retina Tomograph images

An Improved Single Image Haze Removal Algorithm Based on Dark Channel Prior and Histogram Specification

Image contrast enhancement for outdoor machine vision applications

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