Daytime fog detection and density estimation with entropy minimization

Caraffa, Laurent; Tarel, Jean Philippe

doi:10.5194/isprsannals-ii-3-25-2014

Cited by 15 publications

(3 citation statements)

References 18 publications

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“…3) Using MATLAB 2.0 for Development of this Project. We using the traditional methods of both Retinex Algorithm [2,3] and Dark channel Algorithm [8], to compare with the propose method (R+WT method). The proposed method is having the process of combining retinex and wavelet transform algorithm…”

Section: Analysis Of Fog Removing and Mathematical Analysis Of Retinex Algorithmmentioning

confidence: 99%

Fog-Removing Method for the Monitoring Traffic Image along with Mathematical Analysis of Retinex Algorithm

Deepika¹

2019

IJRASET

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In this paper, we have a tendency to propose an improved fog-removing technique along with mathematical analysis of retinex algorithm so as to form the pictures changing into more clear and less difficult to recognize. The projected technique combines the retinex algorithmic rule and wavelet transform algorithmic rule. The projected technique firstly uses Retinex algorithmic rule to reinforce the image, then the wavelet transform algorithmic rule is employed to reinforce the details of the image, finally, a clear image that is removed fog will be obtained once reduce the non-necessary coefficients. Through analyzing the PSNR (Peak S/N Ratio) of the image contrast, the pictures that are processed by our proposed technique have the PSNR values above the standard Retinex algorithms. In this, we intend to build up a novel picture defogging calculation by specifically anticipating the haze thickness of improved pictures as opposed to receiving earlier suppositions or requirements. The proposed method reduces the halo artifacts by adaptively limiting the boundary of an arbitrary haze image. A new multi-scale image fusion method for single image dehazing has also been proposed to produce a more natural visual recovery effect. The results show that this method outperforms state-of-the-art haze removal methods in terms of both efficiency and the dehazing visual effect.I. INTRODUCTION Ideal Transmission Estimation by means of Fog Density Perception for Efficient Single Fog Image Restoration by using the Retinex Algorithm is introduced to reduce traffic issues in the presence fog( like accidents that are happening because of weather changes ). Nowadays, this Fog is very serious in everywhere, because of the pollution, climatic issues and so on. These Foggy climates will create issues like whitening effect, image degradation all these kinds of situations will leads to a serious impact on transportation systems and outdoor visibility systems. So, there is a need for creative requirements to solve these problems and to deal with these climatic issues. Especially with this fog, to get a clear and perfect visibility. With the continuous development of computer hardware and software technology, it became possible to solve this problem (removing fog from the images). Before, We have existing methods to do reduce these fog issues By using image contrast enhancement method (using adjustments in grey values -but quality of image is missing), By using image restoration method (by reducing the depth information of an image helps ) etc,. but every method is having their disadvantages and those are not giving clear and realistic images to visibility system. In this method of removing fog from single images, The structure is as follows : Introduction, literature survey, proposed method introduction, and the experiment results & Conclusion. A. Retinex Theory and Retinex AlgorithmThe fundamental Retinex model depends on the supposition that the HVS works with three retinal-cortical frameworks, each preparing autonomously the low, center, an...

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Section: Analysis Of Fog Removing and Mathematical Analysis Of Retinex Algorithmmentioning

confidence: 99%

Fog-Removing Method for the Monitoring Traffic Image along with Mathematical Analysis of Retinex Algorithm

Deepika¹

2019

IJRASET

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“…Busch et al presented a new technique to estimate the visibility distance in a foggy condition, which is based on a psychovisual model and on contrast estimation using wavelet transform [9]. Caraffa et al [10] calculated a slightly more accurate meteorological visibility. The meteorological visibility is calculated from Eq.…”

Section: Related Workmentioning

confidence: 99%

Fast Fog Detection for De-Fogging of Road Driving Images

Jeong

Choi

Kim

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYAdvanced driver assistance system (ADAS) can recognize traffic signals, vehicles, pedestrians, and so on all over the vehicle. However, because the ADAS is based on images taken in an outdoor environment, it is susceptible to ambient weather such as fog. So, preprocessing such as de-fog and de-hazing techniques is required to prevent degradation of object recognition performance due to decreased visibility. But, if such a fog removal technique is applied in an environment where there is little or no fog, the visual quality may be deteriorated due to excessive contrast improvement. And in foggy road environments, typical fog removal algorithms suffer from color distortion. In this paper, we propose a temporal filter-based fog detection algorithm to selectively apply de-fogging method only in the presence of fog. We also propose a method to avoid color distortion by detecting the sky region and applying different methods to the sky region and the non-sky region. Experimental results show that in the actual images, the proposed algorithm shows an average of more than 97% fog detection accuracy, and improves subjective image quality of existing de-fogging algorithms. In addition, the proposed algorithm shows very fast computation time of less than 0.1ms per frame.

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“…The task of determining the visibility of a volcano can be considered in the context of computer vision problems such as determining atmospheric visibility [2,3], detecting changes [4,5] or searching for features typical of to the volcano [6]. The inhomogeneity of the atmosphere at large distances, the large variability of the appearance of the volcano as a function of season and lighting, and the varying rate of change in weather conditions make it difficult to use the first two methods.…”

Section: Introductionmentioning

confidence: 99%

Computer methods of image processing of volcanoes

Urmanov¹,

Kamaev²,

Сорокин³

2017

Proceedings of the IV International Research Conference "Information Technologies in Science, Management, Social Sphere and Med

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Abstract-Video surveillance of volcanoes is often complicated by bad weather conditions. Many images are not informative, since they do not show the observed object, that makes access to high-quality images difficult for experts volcanologists and leads to excessive consumption of disk memory. In this paper, an algorithm for calculating the visibility estimate of volcanoes based on the comparison of parametric edges in images with the reference edges of the observed volcano is considered. The resulting estimate can be used to filter noninformative images.

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Daytime fog detection and density estimation with entropy minimization

Cited by 15 publications

References 18 publications

Fog-Removing Method for the Monitoring Traffic Image along with Mathematical Analysis of Retinex Algorithm

Fog-Removing Method for the Monitoring Traffic Image along with Mathematical Analysis of Retinex Algorithm

Fast Fog Detection for De-Fogging of Road Driving Images

Computer methods of image processing of volcanoes

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