AB-chromaticity histogram analysis works well most of the time, but it may not work well when the color cast is not severe. To overcome this problem, we propose an improved, two-step automatic cast-detection method. F...AB-chromaticity histogram analysis works well most of the time, but it may not work well when the color cast is not severe. To overcome this problem, we propose an improved, two-step automatic cast-detection method. First, we compute the RGB color variance to evaluate the quality of the input image. If this variance is very small, we extract near-neutral color areas and compute the local ab-chromaticity histogram. We use this local ab-chromaticity histogram to evaluate the quality of the input image. This method has been tested in ZTE＇ s video surveil- lance system. The results show that the proposed method pro- duces better results based on subjective evaluation and is more efficient in various conditions.展开更多
Nighttime image dehazing aims to remove the effect of haze on the images captured in nighttime,which however,raises new challenges such as severe color distortion,more complex lighting conditions,and lower contrast.In...Nighttime image dehazing aims to remove the effect of haze on the images captured in nighttime,which however,raises new challenges such as severe color distortion,more complex lighting conditions,and lower contrast.Instead of estimating the transmission map and atmospheric light that are difficult to be accurately acquired in nighttime,we propose a nighttime image dehazing method composed of a color cast removal and a dual path multi-scale fusion algorithm.We first propose a human visual system(HVS)inspired color correction model,which is effective for removing the color deviation on nighttime hazy images.Then,we propose to use dual path strategy that includes an underexposure and a contrast enhancement path for multi-scale fusion,where the weight maps are achieved by selecting appropriate exposed areas under Gaussian pyramids.Extensive experiments demonstrate that the visual effect of the hazy nighttime images in real-world datasets can be significantly improved by our method regarding contrast,color fidelity,and visibility.In addition,our method outperforms the state-of-the-art methods qualitatively and quantitatively.展开更多
文摘AB-chromaticity histogram analysis works well most of the time, but it may not work well when the color cast is not severe. To overcome this problem, we propose an improved, two-step automatic cast-detection method. First, we compute the RGB color variance to evaluate the quality of the input image. If this variance is very small, we extract near-neutral color areas and compute the local ab-chromaticity histogram. We use this local ab-chromaticity histogram to evaluate the quality of the input image. This method has been tested in ZTE＇ s video surveil- lance system. The results show that the proposed method pro- duces better results based on subjective evaluation and is more efficient in various conditions.
基金supported by Higher Education Scientific Research Project of Ningxia(NGY2017009)。
文摘Nighttime image dehazing aims to remove the effect of haze on the images captured in nighttime,which however,raises new challenges such as severe color distortion,more complex lighting conditions,and lower contrast.Instead of estimating the transmission map and atmospheric light that are difficult to be accurately acquired in nighttime,we propose a nighttime image dehazing method composed of a color cast removal and a dual path multi-scale fusion algorithm.We first propose a human visual system(HVS)inspired color correction model,which is effective for removing the color deviation on nighttime hazy images.Then,we propose to use dual path strategy that includes an underexposure and a contrast enhancement path for multi-scale fusion,where the weight maps are achieved by selecting appropriate exposed areas under Gaussian pyramids.Extensive experiments demonstrate that the visual effect of the hazy nighttime images in real-world datasets can be significantly improved by our method regarding contrast,color fidelity,and visibility.In addition,our method outperforms the state-of-the-art methods qualitatively and quantitatively.
