A Point Light Source Interference Removal Method for Image Dehazing

Yan, Yanyang; Zhang, Shengdong; Ju, Mingye; Ren, Wenqi; Wang, Rui; Guo, Yuanfang

doi:10.1109/cvprw50498.2020.00445

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…We get the pixel with highest brightness as the center of the hypothetic point light source and get its brightness as the central luminance. Generally, pixels close to a point light source possess higher intensity than those faraway [21]. Pixels on the same circle should have the same luminance because they have the same distance from the center of the sphere shown in (a).…”

Section: Fused Ambient Lightmentioning

confidence: 99%

Nighttime Haze Removal Using Saliency-Oriented Ambient Light And Transmission Estimation

Lin

Wang

Zhang

2021

2021 IEEE International Conference on Image Processing (ICIP)

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The ambient light and transmission of light source regions and non-light source regions have different physical properties in nighttime scenes. However, existing nighttime dehazing algorithms ignored this phenomenon and got oversaturated results looked like in the daytime. In this paper, we propose a nighttime dehazing method using fused ambient light and transmission based on saliency detection. We first propose a multi-scale saliency detection algorithm to differentiate light source regions and non-light source regions. Then we estimate ambient light and transmission of the two regions based on different methods to acquire the fused ambient light and fused transmission. Finally, we get the haze-free results based on the nighttime imaging model. Experimental results show that our method outperforms other state-of-the-art methods in color recovery and our haze-free results still look like in the nighttime.

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Section: Fused Ambient Lightmentioning

confidence: 99%

Nighttime Haze Removal Using Saliency-Oriented Ambient Light And Transmission Estimation

Lin

Wang

Zhang

2021

2021 IEEE International Conference on Image Processing (ICIP)

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VRHI: Visibility Restoration for Hazy Images Using a Haze Density Model

Chen

Liu

et al. 2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Nighttime Image Dehazing Based on Point Light Sources

Yao

Zhang

et al. 2022

Applied Sciences

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Images routinely suffer from quality degradation in fog, mist, and other harsh weather conditions. Consequently, image dehazing is an essential and inevitable pre-processing step in computer vision tasks. Image quality enhancement for special scenes, especially nighttime image dehazing is extremely well studied for unmanned driving and nighttime surveillance, while the vast majority of dehazing algorithms in the past were only applicable to daytime conditions. After observing a large number of nighttime images, artificial light sources have replaced the position of the sun in daytime images and the impact of light sources on pixels varies with distance. This paper proposed a novel nighttime dehazing method using the light source influence matrix. The luminosity map can well express the photometric difference value of the picture light source. Then, the light source influence matrix is calculated to divide the image into near light source region and non-near light source region. Using the result of two regions, the two initial transmittances obtained by dark channel prior are fused by edge-preserving filtering. For the atmospheric light term, the initial atmospheric light value is corrected by the light source influence matrix. Finally, the final result is obtained by substituting the atmospheric light model. Theoretical analysis and comparative experiments verify the performance of the proposed image dehazing method. In terms of PSNR, SSIM, and UQI, this method improves 9.4%, 11.2%, and 3.3% over the existed night-time defogging method OSPF. In the future, we will explore the work from static picture dehazing to real-time video stream dehazing detection and will be used in detection on potential applications.

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A Point Light Source Interference Removal Method for Image Dehazing

Cited by 3 publications

References 21 publications

Nighttime Haze Removal Using Saliency-Oriented Ambient Light And Transmission Estimation

Nighttime Haze Removal Using Saliency-Oriented Ambient Light And Transmission Estimation

VRHI: Visibility Restoration for Hazy Images Using a Haze Density Model

Nighttime Image Dehazing Based on Point Light Sources

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