Recolorizing dark regions to enhance night surveillance video

Soumya, T; Thampi, Sabu M.

doi:10.1007/s11042-016-4141-4

Cited by 3 publications

(4 citation statements)

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“…Rao et al [ 30 ] proposed fusing night video and daytime background brightness in a gradient-domain to obtain enhanced images. Soumya et al [ 31 ] used wavelet fusion to fuse night video frames with daytime background frames to effectively highlight the illumination area. Lee et al [ 32 ] divided the video frame into the dark target area and bright background area and then fused the adjusted dark area with the bright area to enhance the video.…”

Section: Related Workmentioning

confidence: 99%

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior

Guo

Jia

Yang

et al. 2021

Sensors

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In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications.

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Section: Related Workmentioning

confidence: 99%

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior

Guo

Jia

Yang

et al. 2021

Sensors

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“…Haar wavelet, orthogonal normalized wavelet, is used here. Haar wavelet transform is interpreted as follows: For example, there are four numbers a= [8,7,6,9], and b [4] arrays are used to save the results. Then the results of first-order Haar wavelet transform are as follows:…”

Section: Algorithmic Stepsmentioning

confidence: 99%

“…That is to say, two numbers are taken from the array in turn, and their sum and difference are calculated, and half of the sum and difference are stored in the first half and the second half of the array in turn. For example, if there is a [8], the result of one-dimensional Haar wavelet transform is preserved in b [8], then the result of first-order Haar wavelet transform is obtained.…”

Section: Algorithmic Stepsmentioning

confidence: 99%

“…Therefore, many scholars have done a lot of work in the traditional field of video image enhancement [7], and invented many algorithms for low illumination video image enhancement. By fusing the information of the daytime background image to restore the surrounding scene [8],but the visual effect of the video is poor. Subsequently, some scholars improved the algorithm.…”

Section: Introductionmentioning

confidence: 99%

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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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