Improved video defogging based on fog theory

Haibo, 刘海波 Liu; Jie, 杨杰 Yang; Zheng-ping, 吴正平 Wu; Qing-nian, 张庆年 Zhang; Yong, 邓 勇 Deng

doi:10.3788/ope.20162407.1789

Cited by 1 publication

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“…Night video enhancement using improved dark channel prior proposes an improved dark channel priori model and combines it with local smoothing and image Gauss pyramid operators. It not only enhances the visual effect of low illumination video image, but also effectively enhances the edges of the image [9][10]. In image processing, many real images are taken under ambiguous conditions, and need to be processed to obtain better quality.…”

Section: Introductionmentioning

confidence: 99%

Low Illumination Video Image Enhancement

Yang

Kasabov

2020

IEEE Photonics J.

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Due to weather conditions, brightness conditions, capture equipment and other factors, leads to video unclear or even abnormally confused, which is not conducive to monitoring, and can not meet the needs of applications. Based on the actual data of night video surveillance, this paper proposes a new low illumination video image enhancement algorithm, which overcomes the existing problems. We analyze the characteristics of low illumination video image, and use HSV color space instead of traditional RGB space to enhance the robustness of video contrast and color distortion. At the same time, we use wavelet image fusion to highlight the details of video image, so the enhanced video has higher clarity and visual effect. Compared with other four algorithms, the proposed algorithm outperforms the above algorithms in subjective evaluation and objective evaluation. At the same time, compared with other algorithms, the proposed algorithm has faster processing time for each frame. Experiments show that the algorithm can effectively improve the overall brightness and contrast of video images, and avoid the over-enhancement of bright areas near the light source, which can meet the practical application requirements of video surveillance.

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Section: Introductionmentioning

confidence: 99%