Single image haze removal based on fusion darkness channel prior

Xiang, Ruxi; Wu, Feng; Jiang, Xiaoyan

doi:10.1142/s0217984917400371

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…According to the relative size of the wavelengths of the incident light to the low and the diameter of the haze, scattering of the tiny particles floating in the air is divided to two types: Mie and Rayleigh scattering. In 1975, McCartney proposed a famous theory of atmospheric scattering theory (McCartney, 1977), which claims that under the condition of more severe haze weather, the atmospheric scattering model is mainly composed of two models, one is the incident light attenuation model, and another is the atmospheric optical imaging model (Zhu et al, 2017). Figure 1 shows the outline of the model.…”

Section: Atmospheric Scattering Modelmentioning

confidence: 99%

A fast video haze removal algorithm via mixed transmissivity optimisation

et al. 2019

IJES

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Image restoration is an important approach to image and video defogging. One of the most popular algorithms for image restoration is dark channel prior. However, when the algorithm is applied to outdoor digital webcams with limited computing resources, its real-time performance probably cannot be guaranteed. To address the above issue, this paper presents a fast video haze removal algorithm based on mixed optimised transmissivity to improve the time performance of the dark channel prior algorithm. The proposed algorithm combines guided filter and median filter and replaces the soft matting procedure in the classical dark channel prior algorithm. A set of experiments are performed to evaluate the real-time performance and effectiveness of the algorithm. The results show that our proposed improved algorithm can significantly improve the speed of video defogging, without sacrificing much effectiveness in identification of target objects.

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Section: Atmospheric Scattering Modelmentioning

confidence: 99%

A fast video haze removal algorithm via mixed transmissivity optimisation

et al. 2019

IJES

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“…At present, there are mainly two mainstream image defogging traditional algorithms: One is the method based on image enhancement [4][5][6][7], which processes image pixels directly in the spatial domain; the other is the method based on image restoration [8][9][10][11][12][13], which establishes first atmospheric scattering degradation model of the image and obtains fogless image by reversing the imaging process.…”

Section: Introductionmentioning

confidence: 99%

“…Singh and Kumar [12] raised a kind of visibility restoration model, which could solve the problem of glow effect in the region of sky and improve the computation speed and edge preservation taking the advantage of DCP, bright channel prior (BCP), and gain interference filter, with greater potential of real-time application. Zhu et al [13] established an effective dark channel model integrating information of various dark channels by analyzing various dark channel methods, which could estimate the transmission diagram of the input image, refine it with improved steerable filter, and restore the radiation image with single color atmospheric scattering model. Based on the fusion method, Ancuti C.O.…”

Section: Introductionmentioning

confidence: 99%

Defogging Technology Based on Dual-Channel Sensor Information Fusion of Near-Infrared and Visible Light

Yuan

Peng

et al. 2020

Journal of Sensors

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Since the method to remove fog from images is complicated and detail loss and color distortion could occur to the defogged images, a defogging method based on near-infrared and visible image fusion is put forward in this paper. The algorithm in this paper uses the near-infrared image with rich details as a new data source and adopts the image fusion method to obtain a defog image with rich details and high color recovery. First, the colorful visible image is converted into HSI color space to obtain an intensity channel image, color channel image, and saturation channel image. The intensity channel image is fused with a near-infrared image and defogged, and then it is decomposed by Nonsubsampled Shearlet Transform. The obtained high-frequency coefficient is filtered by preserving the edge with a double exponential edge smoothing filter, while low-frequency antisharpening masking treatment is conducted on the low-frequency coefficient. The new intensity channel image could be obtained based on the fusion rule and by reciprocal transformation. Then, in color treatment of the visible image, the degradation model of the saturation image is established, which estimates the parameters based on the principle of dark primary color to obtain the estimated saturation image. Finally, the new intensity channel image, the estimated saturation image, and the primary color image are reflected to RGB space to obtain the fusion image, which is enhanced by color and sharpness correction. In order to prove the effectiveness of the algorithm, the dense fog image and the thin fog image are compared with the popular single image defogging and multiple image defogging algorithms and the visible light-near infrared fusion defogging algorithm based on deep learning. The experimental results show that the proposed algorithm is better in improving the edge contrast and the visual sharpness of the image than the existing high-efficiency defogging method.

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Desmogging of still smoggy images using a novel channel prior

Jain

Kumar

2020

J Ambient Intell Human Comput

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Single image haze removal based on fusion darkness channel prior

Cited by 9 publications

References 9 publications

A fast video haze removal algorithm via mixed transmissivity optimisation

A fast video haze removal algorithm via mixed transmissivity optimisation

Defogging Technology Based on Dual-Channel Sensor Information Fusion of Near-Infrared and Visible Light

Desmogging of still smoggy images using a novel channel prior

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