Underwater images often exhibit severe color deviations and degraded visibility,which limits many practical applications in ocean engineering.Although extensive research has been conducted into underwater image enhanc...Underwater images often exhibit severe color deviations and degraded visibility,which limits many practical applications in ocean engineering.Although extensive research has been conducted into underwater image enhancement,little of which demonstrates the significant robustness and generalization for diverse real-world underwater scenes.In this paper,we propose an adaptive color correction algorithm based on the maximum likelihood estimation of Gaussian parameters,which effectively removes color casts of a variety of underwater images.A novel algorithm using weighted combination of gradient maps in HSV color space and absolute difference of intensity for accurate background light estimation is proposed,which circumvents the influence of white or bright regions that challenges existing physical model-based methods.To enhance contrast of resultant images,a piece-wise affine transform is applied to the transmission map estimated via background light differential.Finally,with the estimated background light and transmission map,the scene radiance is recovered by addressing an inverse problem of image formation model.Extensive experiments reveal that our results are characterized by natural appearance and genuine color,and our method achieves competitive performance with the state-of-the-art methods in terms of objective evaluation metrics,which further validates the better robustness and higher generalization ability of our enhancement model.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
基金supported by Higher Education Scientific Research Project of Ningxia(NGY2017009).
文摘Underwater images often exhibit severe color deviations and degraded visibility,which limits many practical applications in ocean engineering.Although extensive research has been conducted into underwater image enhancement,little of which demonstrates the significant robustness and generalization for diverse real-world underwater scenes.In this paper,we propose an adaptive color correction algorithm based on the maximum likelihood estimation of Gaussian parameters,which effectively removes color casts of a variety of underwater images.A novel algorithm using weighted combination of gradient maps in HSV color space and absolute difference of intensity for accurate background light estimation is proposed,which circumvents the influence of white or bright regions that challenges existing physical model-based methods.To enhance contrast of resultant images,a piece-wise affine transform is applied to the transmission map estimated via background light differential.Finally,with the estimated background light and transmission map,the scene radiance is recovered by addressing an inverse problem of image formation model.Extensive experiments reveal that our results are characterized by natural appearance and genuine color,and our method achieves competitive performance with the state-of-the-art methods in terms of objective evaluation metrics,which further validates the better robustness and higher generalization ability of our enhancement model.
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.
