针对海上图像利用带色彩恢复的多尺度Retinex算法(multi-scale Retinex with color restoration,MSRCR)不能有效去除雾及存在颜色纠偏过度问题,提出了一种基于全局亮度自适应均衡化的海上图像改进MSRCR算法。该算法首先计算海上雾天图...针对海上图像利用带色彩恢复的多尺度Retinex算法(multi-scale Retinex with color restoration,MSRCR)不能有效去除雾及存在颜色纠偏过度问题,提出了一种基于全局亮度自适应均衡化的海上图像改进MSRCR算法。该算法首先计算海上雾天图像的取反图;其次对原图像和取反后图像进行MSRCR运算;然后利用全局亮度自适应直方图均衡化处理,并将处理后的亮度与经MSRCR处理后的反射分量进行低频信号线性叠加;最后计算叠加后图像的均值和标准差,并采用自适应拉伸图像灰度实现图像色彩对比度的提升。实验证明该算法处理后的图像,前景突出、细节清晰、色彩丰富,对于海上图像除雾具有一定的意义。展开更多
Melt ponds on Arctic sea ice are of great significance in the study of the heat balance in the ocean mixed layer, mass and salt balances of Arctic sea ice, and other aspects of the earth-atmosphere system. During the ...Melt ponds on Arctic sea ice are of great significance in the study of the heat balance in the ocean mixed layer, mass and salt balances of Arctic sea ice, and other aspects of the earth-atmosphere system. During the 7th Chinese National Arctic Research Expedition, aerial photographs were taken from an Unmanned Aerial Vehicle over an ice floe in the Canada Basin. Using threshold discrimination and three-dimensional modeling, we estimated a melt pond fraction of 1.63% and a regionally averaged surface roughness of 0.12 for the study area. In view- of the particularly foggy environment of the Arctic, aerial images were defogged using an improved dark channel prior based image defog algorithm, especially adapted for the special conditions of sea ice images. An aerial photo mosaic was generated, melt ponds were identified from the mosaic image and melt pond fractions were calculated. Three-dimensional modeling techniques were used to generate a digital elevation model allowing relative elevation and roughness of the sea ice surface to be estimated. Analysis of the relationship between the distributions of melt ponds and sea ice surface roughness show-s that melt ponds are smaller on sea ice with higher surface roughness, while broader melt ponds usually occur in areas where sea ice surface roughness is lower.展开更多
The captured outdoor images and videos may appear blurred due to haze,fog,and bad weather conditions.Water droplets or dust particles in the atmosphere cause the light to scatter,resulting in very limited scene discer...The captured outdoor images and videos may appear blurred due to haze,fog,and bad weather conditions.Water droplets or dust particles in the atmosphere cause the light to scatter,resulting in very limited scene discernibility and deterioration in the quality of the image captured.Currently,image dehazing has gainedmuch popularity because of its usability in a wide variety of applications.Various algorithms have been proposed to solve this ill-posed problem.These algorithms provide quite promising results in some cases,but they include undesirable artifacts and noise in haze patches in adverse cases.Some of these techniques take unrealistic processing time for high image resolution.In this paper,to achieve real-time halo-free dehazing,fast and effective single image dehazing we propose a simple but effective image restoration technique using multiple patches.It will improve the shortcomings of DCP and improve its speed and efficiency for high-resolution images.A coarse transmissionmap is estimated by using the minimumof different size patches.Then a cascaded fast guided filter is used to refine the transmission map.We introduce an efficient scaling technique for transmission map estimation,which gives an advantage of very low-performance degradation for a highresolution image.For performance evaluation,quantitative,qualitative and computational time comparisons have been performed,which provide quiet faithful results in speed,quality,and reliability of handling bright surfaces.展开更多
文摘针对海上图像利用带色彩恢复的多尺度Retinex算法(multi-scale Retinex with color restoration,MSRCR)不能有效去除雾及存在颜色纠偏过度问题,提出了一种基于全局亮度自适应均衡化的海上图像改进MSRCR算法。该算法首先计算海上雾天图像的取反图;其次对原图像和取反后图像进行MSRCR运算;然后利用全局亮度自适应直方图均衡化处理,并将处理后的亮度与经MSRCR处理后的反射分量进行低频信号线性叠加;最后计算叠加后图像的均值和标准差,并采用自适应拉伸图像灰度实现图像色彩对比度的提升。实验证明该算法处理后的图像,前景突出、细节清晰、色彩丰富,对于海上图像除雾具有一定的意义。
基金funded by the National Natural Science Foundation of China (Grant no.41276193)the Global Change Research Program of China (Grant no.2015CB953901)the National Key Research and Development Program of China (Grant no.2016YFC1402704)
文摘Melt ponds on Arctic sea ice are of great significance in the study of the heat balance in the ocean mixed layer, mass and salt balances of Arctic sea ice, and other aspects of the earth-atmosphere system. During the 7th Chinese National Arctic Research Expedition, aerial photographs were taken from an Unmanned Aerial Vehicle over an ice floe in the Canada Basin. Using threshold discrimination and three-dimensional modeling, we estimated a melt pond fraction of 1.63% and a regionally averaged surface roughness of 0.12 for the study area. In view- of the particularly foggy environment of the Arctic, aerial images were defogged using an improved dark channel prior based image defog algorithm, especially adapted for the special conditions of sea ice images. An aerial photo mosaic was generated, melt ponds were identified from the mosaic image and melt pond fractions were calculated. Three-dimensional modeling techniques were used to generate a digital elevation model allowing relative elevation and roughness of the sea ice surface to be estimated. Analysis of the relationship between the distributions of melt ponds and sea ice surface roughness show-s that melt ponds are smaller on sea ice with higher surface roughness, while broader melt ponds usually occur in areas where sea ice surface roughness is lower.
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2021-2020-0-01832)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)and the Soonchunhyang University Research Fund.
文摘The captured outdoor images and videos may appear blurred due to haze,fog,and bad weather conditions.Water droplets or dust particles in the atmosphere cause the light to scatter,resulting in very limited scene discernibility and deterioration in the quality of the image captured.Currently,image dehazing has gainedmuch popularity because of its usability in a wide variety of applications.Various algorithms have been proposed to solve this ill-posed problem.These algorithms provide quite promising results in some cases,but they include undesirable artifacts and noise in haze patches in adverse cases.Some of these techniques take unrealistic processing time for high image resolution.In this paper,to achieve real-time halo-free dehazing,fast and effective single image dehazing we propose a simple but effective image restoration technique using multiple patches.It will improve the shortcomings of DCP and improve its speed and efficiency for high-resolution images.A coarse transmissionmap is estimated by using the minimumof different size patches.Then a cascaded fast guided filter is used to refine the transmission map.We introduce an efficient scaling technique for transmission map estimation,which gives an advantage of very low-performance degradation for a highresolution image.For performance evaluation,quantitative,qualitative and computational time comparisons have been performed,which provide quiet faithful results in speed,quality,and reliability of handling bright surfaces.
基金supported by Beijing Municipal Education Commission Science and Technology Development Plan key project(KZ201210005007)the Beijing Municipal Education Commission Science and Technology Development Plan project(KM201010005011,KM201310005020)
