Low-light image haze removal with light segmentation and nonlinear image depth estimation

doi:10.1049/ipr2.12513

CIOMP OpenIR

	Low-light image haze removal with light segmentation and nonlinear image depth estimation
	J. W. Lv; F. Qian and B. Zhang
	2022
发表期刊	Iet Image Processing
ISSN	1751-9659
卷号	16 期号:10 页码:2623-2637
摘要	Hazy image obtained in the low-light environment has the characteristics of low contrast, non-uniform illumination, color cast and much noise. In this paper, a method is put forward which can be properly applied to recover low-light hazy images. The original image is first decomposed into glow layer and haze layer with a modified color channel transformation for glow artifacts and color balanced. A new light segmentation function is proposed next by using gamma correction of channel difference and setting threshold levels to determine if the pixel belongs to light source regions. Then the ambient illuminance map is estimated using maximum reflectance prior to computing the atmosphere light in the light and non-light regions. Finally, a novel nonlinear image depth estimation model is established to build the relationship between the image depth map and three image features including luminance, saturation and gradient map for the light areas. The experimental results prove that the dehazing algorithm is reliable for removing haze and glow artifacts of active light sources, reducing much noise and improving the visibility.
DOI	10.1049/ipr2.12513
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收录类别	sci ; ei
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/66821
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	J. W. Lv,F. Qian and B. Zhang. Low-light image haze removal with light segmentation and nonlinear image depth estimation[J]. Iet Image Processing,2022,16(10):2623-2637.
APA	J. W. Lv,&F. Qian and B. Zhang.(2022).Low-light image haze removal with light segmentation and nonlinear image depth estimation.Iet Image Processing,16(10),2623-2637.
MLA	J. W. Lv,et al."Low-light image haze removal with light segmentation and nonlinear image depth estimation".Iet Image Processing 16.10(2022):2623-2637.

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