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Single image dehazing using a new color channel
Journal of Visual Communication and Image Representation ( IF 2.6 ) Pub Date : 2020-12-21 , DOI: 10.1016/j.jvcir.2020.103008
Geet Sahu , Ayan Seal , Ondrej Krejcar , Anis Yazidi

Images with hazy scene suffer from low-contrast, which reduces the visible quality of the scene, thus making object detection a more challenging task. Low-contrast can result from foggy weather conditions during image acquisition. Dehazing is a process of removal of haze from the photography of a hazy scene. Single-image dehazing based on dark channel priors are well-known techniques in this field. However, the performance of such techniques is limited to priors or constraints. Moreover, this type of method fails when images have sky-region. So, a method is proposed, which can restore the visibility of hazy images. First, a hazy image is divided into blocks of size 32 × 32, then the score of each block is calculated to select a block having the highest score. Atmospheric light is calculated from the selected block. A new color channel is considered to remove atmospheric scattering, obtained channel value and atmospheric light are then used to calculate the transmission map in the second step. Third, radiance is computed using a transmission map and atmospheric light. The illumination scaling factor is adopted to enhance the quality of a dehazed image in the final step. Experiments are performed on six datasets namely, I-HAZE, O-HAZE, BSDS500, FRIDA, RESIDE dataset and natural images from Google. The proposed method is compared against 11 state-of-the-art methods. The performance is analyzed using fourteen quantitative evaluation metrics. All the results demonstrate that the proposed method outperforms 11 state-of-the-art methods in most of the cases.



中文翻译:

使用新的色彩通道对单个图像进行除雾

具有朦胧场景的图像具有低对比度,这会降低场景的可见质量,从而使目标检测成为一项更具挑战性的任务。图像采集期间的大雾天气可能导致对比度低。除雾是从模糊场景的摄影中去除雾度的过程。基于暗通道先验的单图像去雾是该领域中众所周知的技术。但是,这种技术的性能限于先验或约束。而且,当图像具有天空区域时,这种方法将失败。因此,提出了一种可以恢复模糊图像可视性的方法。首先,将模糊图像划分为大小为32×32的块,然后计算每个块的得分,以选择得分最高的一块。根据所选块计算出大气光。考虑使用新的颜色通道来消除大气散射,然后在第二步中使用获得的通道值和大气光来计算透射图。第三,使用透射图和大气光来计算辐射率。在最终步骤中,采用照明比例因子以提高去雾图像的质量。实验针对六个数据集(即I-HAZE,O-HAZE,BSDS500,FRIDA,RESIDE数据集和Google的自然图像)进行。将所提出的方法与11种最新方法进行了比较。使用十四种定量评估指标来分析性能。所有结果表明,在大多数情况下,所提出的方法优于11种最新方法。然后在第二步中使用获得的通道值和大气光来计算透射率图。第三,使用透射图和大气光来计算辐射率。在最终步骤中,采用照明比例因子以提高去雾图像的质量。实验针对六个数据集(即I-HAZE,O-HAZE,BSDS500,FRIDA,RESIDE数据集和Google的自然图像)进行。将所提出的方法与11种最新方法进行了比较。使用十四种定量评估指标来分析性能。所有结果表明,在大多数情况下,所提出的方法优于11种最新方法。然后在第二步中使用获得的通道值和大气光来计算透射率图。第三,使用透射图和大气光来计算辐射率。在最终步骤中,采用照明比例因子以提高去雾图像的质量。实验针对六个数据集(即I-HAZE,O-HAZE,BSDS500,FRIDA,RESIDE数据集和Google的自然图像)进行。将所提出的方法与11种最新方法进行了比较。使用十四种定量评估指标来分析性能。所有结果表明,在大多数情况下,所提出的方法优于11种最新方法。在最终步骤中,采用照明比例因子以提高去雾图像的质量。实验针对六个数据集(即I-HAZE,O-HAZE,BSDS500,FRIDA,RESIDE数据集和Google的自然图像)进行。将所提出的方法与11种最新方法进行了比较。使用十四种定量评估指标来分析性能。所有结果表明,在大多数情况下,所提出的方法优于11种最新方法。在最终步骤中,采用照明比例因子来提高去雾图像的质量。对六个数据集(I-HAZE,O-HAZE,BSDS500,FRIDA,RESIDE数据集和Google的自然图像)进行了实验。将所提出的方法与11种最新方法进行了比较。使用十四种定量评估指标来分析性能。所有结果表明,在大多数情况下,所提出的方法优于11种最新方法。

更新日期:2020-12-25
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