Outdoor haze has adverse impact on outdoor image quality,including contrast loss and poor visibility.In this paper,a novel dehazing algorithm based on the decomposition strategy is proposed.It combines the advantages ...Outdoor haze has adverse impact on outdoor image quality,including contrast loss and poor visibility.In this paper,a novel dehazing algorithm based on the decomposition strategy is proposed.It combines the advantages of the two-dimensional variational mode decomposition(2DVMD)algorithm and dark channel prior.The original hazy image is adaptively decom-posed into low-frequency and high-frequency images according to the image frequency band by using the 2DVMD algorithm.The low-frequency image is dehazed by using the improved dark channel prior,and then fused with the high-frequency image.Furthermore,we optimize the atmospheric light and transmit-tance estimation method to obtain a defogging effect with richer details and stronger contrast.The proposed algorithm is com-pared with the existing advanced algorithms.Experiment results show that the proposed algorithm has better performance in comparison with the state-of-the-art algorithms.展开更多
The visible-light imaging system used in military equipment is often subjected to severe weather conditions, such as fog, haze, and smoke, under complex lighting conditions at night that significantly degrade the acqu...The visible-light imaging system used in military equipment is often subjected to severe weather conditions, such as fog, haze, and smoke, under complex lighting conditions at night that significantly degrade the acquired images. Currently available image defogging methods are mostly suitable for environments with natural light in the daytime, but the clarity of images captured under complex lighting conditions and spatial changes in the presence of fog at night is not satisfactory. This study proposes an algorithm to remove night fog from single images based on an analysis of the statistical characteristics of images in scenes involving night fog. Color channel transfer is designed to compensate for the high attenuation channel of foggy images acquired at night. The distribution of transmittance is estimated by the deep convolutional network DehazeNet, and the spatial variation of atmospheric light is estimated in a point-by-point manner according to the maximum reflection prior to recover the clear image. The results of experiments show that the proposed method can compensate for the high attenuation channel of foggy images at night, remove the effect of glow from a multi-color and non-uniform ambient source of light, and improve the adaptability and visual effect of the removal of night fog from images compared with the conventional method.展开更多
针对去雾图像边缘细节不够清晰,以及现有U-Net去雾网络大多对频域信息的挖掘不够充分、忽略了不同通道之间的信息交流从而导致结构模糊的问题,提出了频域特征蒸馏的双尺度融合网络来实现单幅图像的有效去雾。在粗尺度特征提取子网中采...针对去雾图像边缘细节不够清晰,以及现有U-Net去雾网络大多对频域信息的挖掘不够充分、忽略了不同通道之间的信息交流从而导致结构模糊的问题,提出了频域特征蒸馏的双尺度融合网络来实现单幅图像的有效去雾。在粗尺度特征提取子网中采用大尺度的卷积核提取图像的纹理信息,利用残差注意力机制增强与雾霾相关的特征。在细尺度高频融合子网中,设计了高频特征蒸馏模块用来细化提取到的结构和边缘信息,并逐步恢复清晰的图像;同时采用交叉融合策略对不同通道的特征进行融合。实验结果表明,与MSTN(Efficient and Accurate Multi-Scale Topological Network)算法相比,在室外图像数据集上的峰值信噪比和结构相似度分别提高了9.98%和4.77%。在不同数据集上的实验结果均表明所提出的方法表现出了更良好的去雾性能。该方法可以有效提高去雾的效果,保留更多的结构信息,具有更好的颜色细节恢复能力。展开更多
基金supported by the National Defense Technology Advance Research Project of China(004040204).
文摘Outdoor haze has adverse impact on outdoor image quality,including contrast loss and poor visibility.In this paper,a novel dehazing algorithm based on the decomposition strategy is proposed.It combines the advantages of the two-dimensional variational mode decomposition(2DVMD)algorithm and dark channel prior.The original hazy image is adaptively decom-posed into low-frequency and high-frequency images according to the image frequency band by using the 2DVMD algorithm.The low-frequency image is dehazed by using the improved dark channel prior,and then fused with the high-frequency image.Furthermore,we optimize the atmospheric light and transmit-tance estimation method to obtain a defogging effect with richer details and stronger contrast.The proposed algorithm is com-pared with the existing advanced algorithms.Experiment results show that the proposed algorithm has better performance in comparison with the state-of-the-art algorithms.
基金supported by a grant from the Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology (Grant No. GZZKFJJ2020004)the National Natural Science Foundation of China (Grant Nos. 61875013 and 61827814)the Natural Science Foundation of Beijing Municipality (Grant No. Z190018)。
文摘The visible-light imaging system used in military equipment is often subjected to severe weather conditions, such as fog, haze, and smoke, under complex lighting conditions at night that significantly degrade the acquired images. Currently available image defogging methods are mostly suitable for environments with natural light in the daytime, but the clarity of images captured under complex lighting conditions and spatial changes in the presence of fog at night is not satisfactory. This study proposes an algorithm to remove night fog from single images based on an analysis of the statistical characteristics of images in scenes involving night fog. Color channel transfer is designed to compensate for the high attenuation channel of foggy images acquired at night. The distribution of transmittance is estimated by the deep convolutional network DehazeNet, and the spatial variation of atmospheric light is estimated in a point-by-point manner according to the maximum reflection prior to recover the clear image. The results of experiments show that the proposed method can compensate for the high attenuation channel of foggy images at night, remove the effect of glow from a multi-color and non-uniform ambient source of light, and improve the adaptability and visual effect of the removal of night fog from images compared with the conventional method.
文摘针对去雾图像边缘细节不够清晰,以及现有U-Net去雾网络大多对频域信息的挖掘不够充分、忽略了不同通道之间的信息交流从而导致结构模糊的问题,提出了频域特征蒸馏的双尺度融合网络来实现单幅图像的有效去雾。在粗尺度特征提取子网中采用大尺度的卷积核提取图像的纹理信息,利用残差注意力机制增强与雾霾相关的特征。在细尺度高频融合子网中,设计了高频特征蒸馏模块用来细化提取到的结构和边缘信息,并逐步恢复清晰的图像;同时采用交叉融合策略对不同通道的特征进行融合。实验结果表明,与MSTN(Efficient and Accurate Multi-Scale Topological Network)算法相比,在室外图像数据集上的峰值信噪比和结构相似度分别提高了9.98%和4.77%。在不同数据集上的实验结果均表明所提出的方法表现出了更良好的去雾性能。该方法可以有效提高去雾的效果,保留更多的结构信息,具有更好的颜色细节恢复能力。
