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

doi:10.1049/ipr2.12513

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	Low-light image haze removal with light segmentation and nonlinear image depth estimation
	J. W. Lv; F. Qian and B. Zhang
刊名	Iet Image Processing
	2022
卷号	16 期号:10 页码:2623-2637
ISSN号	1751-9659
DOI	10.1049/ipr2.12513
英文摘要	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.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[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.