Video dehazing for surveillance unmanned aerial vehicle

Cheng, Yue; Niu, Wensheng; Zhai, Zhengjun

doi:10.1109/dasc.2016.7777960

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…However, image dehazing through gamma correction has been hardly explored in the past. In [28], the authors proposed an adaptive gamma correction scheme to refine the transmission map t (x ) before inverting model (1). The same idea was further improved in [29] through Laplacian-based techniques.…”

Section: Related Workmentioning

confidence: 99%

“…We selected the first set of hazy images on which FADE was trained to compare the performance of the different techniques. This dataset is publicly available 1 . Results of the mean FADE values scored across the considered 500 images, after processing them with the same set of dehazing techniques as in previous section, are shown in Table 2.…”

Section: No-reference Image Quality Assessmentmentioning

confidence: 99%

“…In this context, the image processing problem concerned with removing the effect of foggy conditions is known as image dehazing. The availability of effective image dehazing techniques can have a positive impact in computer vision tasks that need to be performed in outdoor scenarios, such as surveillance [1], remote sensing [2,3], or autonomous driving under bad-weather conditions [4].…”

Section: Introductionmentioning

confidence: 99%

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Image dehazing by artificial multiple-exposure image fusion

2018

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Bad weather conditions can reduce visibility on images acquired outdoors, decreasing their visual quality. The image processing task concerned with the mitigation of this effect is known as image dehazing. In this paper we present a new image dehazing technique that can remove the visual degradation due to haze without relying on the inversion of a physical model of haze formation, but respecting its main underlying assumptions. Hence, the proposed technique avoids the need of estimating depth in the scene, as well as costly depth map refinement processes. To achieve this goal, the original hazy image is first artificially under-exposed by means of a sequence of gamma-correction operations. The resulting set of multiplyexposed images is merged into a haze-free result through a multi-scale Laplacian blending scheme. A detailed experimental evaluation is presented in terms of both qualitative and quantitative analysis. The obtained results indicate that the fusion of artificially under-exposed images can effectively remove the effect of haze, even in challenging situations where other current image dehazing techniques fail to produce good-quality results. An implementation of the technique is open-sourced for reproducibility (https://github.com/agaldran/amef_dehazing).

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Section: Related Workmentioning

confidence: 99%

Section: No-reference Image Quality Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image dehazing by artificial multiple-exposure image fusion

2018

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“…The transmission map is enhanced with a different filter and soft matting, and median filter in the next step. According to [26], the transmission map is updated, as shown in ( 11), (12).…”

Section: Proposed Methodologymentioning

confidence: 99%

Multi-scale fusion for underwater image enhancement using multi-layer perceptron

Sudhakara

Meena

2021

IJ-AI

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<span id="docs-internal-guid-54b35aa6-7fff-0992-ed4c-aca4d05cfcfa"><span>Underwater image enhancement (UIE) is an imperative computer vision activity with many applications and different strategies proposed in recent years. Underwater images are firmly low in quality by a mixture of noise, wavelength dependency, and light attenuation. This paper depicts an effective strategy to improve the quality of degraded underwater images. Existing methods for dehazing in the literature considering dark channel prior utilize two separate phases for evaluating the transmission map (i.e., transmission estimation and transmission refinement). Accurate restoration is not possible with these methods and takes more computational time. A proposed three-step method is an imaging approach that does not need particular hardware or underwater conditions. First, we utilize the multi-layer perceptron (MLP) to comprehensively evaluate transmission maps by base channel, followed by contrast enhancement. Furthermore, a gamma-adjusted version of the MLP recovered image is derived. Finally, the multi-scale fusion method was applied to two attained images. The standardized weight is computed for the two images with three different weights in the fusion process. The quantitative results show that significantly our approach gives the better result with the difference of 0.536, 2.185, and 1.272 for PCQI, UCIQE, and UIQM metrics, respectively, on a single underwater image benchmark dataset. The qualitative results also give better results compared with the state-of-the-art techniques.</span></span>

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“…Defogging is, thus, becoming an increasingly desirable technique for both computational photography and computer vision tasks, and it has been extensively studied. Many studies [1–37] have made significant progress in the single‐image defogging field. The early approaches focused on image enhancement [6–9], whereas the later approaches developed handcrafted features based on the statistics of clear images, such as Colour‐Lines [15], DCP [16], and boundary constraint [29], methods.…”

Section: Introductionmentioning

confidence: 99%

Image defogging based on amended dark channel prior and 4‐directional L₁regularisation

Yang

Long

et al. 2021

IET Image Processing

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The dark channel prior (DCP) algorithm has been widely used in the field of image defogging because of its simple theory and clear restoration result. However, the DCP algorithm has significant limitations. This study clarifies the relationship between halo artfacts and the size of the dark channel patch of the DCP algorithm and analyses the reason why the colour of close‐range white objects appears distorted in the restored images. An amended DCP method is then proposed to solve these problems, utilising a locally variable weighted 4‐directional L1 regularisation and a corresponding parallel algorithm to optimise the transmission. A deep neural network, 4DL1R‐net, is then trained to further enhance the processing speed. Extensive experiments demonstrate that this method is effective. The proposed method can obtain clear details, maintain the natural clarity of images, and achieve significant improvements over state‐of‐the‐art methods.

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Video dehazing for surveillance unmanned aerial vehicle

Cited by 7 publications

References 9 publications

Image dehazing by artificial multiple-exposure image fusion

Image dehazing by artificial multiple-exposure image fusion

Multi-scale fusion for underwater image enhancement using multi-layer perceptron

Image defogging based on amended dark channel prior and 4‐directional L₁regularisation

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Video dehazing for surveillance unmanned aerial vehicle

Cited by 7 publications

References 9 publications

Image dehazing by artificial multiple-exposure image fusion

Image dehazing by artificial multiple-exposure image fusion

Multi-scale fusion for underwater image enhancement using multi-layer perceptron

Image defogging based on amended dark channel prior and 4‐directional L1regularisation

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Image defogging based on amended dark channel prior and 4‐directional L₁regularisation