Kim Changwon
Korean Intellectual Property Office, Daejeon 35208, Korea.
Entropy (Basel). 2021 Oct 30;23(11):1438. doi: 10.3390/e23111438.
Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility. This problem has been extensively studied in the literature, and the most popular technique is dark channel prior (). However, dark channel prior tends to underestimate transmissions of bright areas or objects, which may cause color distortions during dehazing. This paper proposes a new single-image dehazing method that combines dark channel prior with bright channel prior in order to overcome the limitations of dark channel prior. A patch-based robust atmospheric light estimation was introduced in order to divide image into regions to which the assumption and the assumption are applied. Moreover, region adaptive haze control parameters are introduced in order to suppress the distortions in a flat and bright region and to increase the visibilities in a texture region. The flat and texture regions are expressed as probabilities by using local image entropy. The performance of the proposed method is evaluated by using synthetic and real data sets. Experimental results show that the proposed method outperforms the state-of-the-art image dehazing method both visually and numerically.
在计算机视觉应用的预处理中，去除图像雾霭至关重要，因为在雾或雪等恶劣天气条件下拍摄的室外图像能见度较差。这个问题在文献中已经得到了广泛研究，最流行的技术是暗通道先验（）。然而，暗通道先验往往会低估明亮区域或物体的透射率，这可能会在去雾过程中导致颜色失真。本文提出了一种新的单图像去雾方法，该方法将暗通道先验与亮通道先验相结合，以克服暗通道先验的局限性。引入了一种基于块的鲁棒大气光估计方法，以便将图像划分为应用不同假设的区域。此外，引入了区域自适应雾霭控制参数，以抑制平坦明亮区域的失真，并提高纹理区域的能见度。通过使用局部图像熵，将平坦区域和纹理区域表示为概率。使用合成数据集和真实数据集对所提方法的性能进行了评估。实验结果表明，所提方法在视觉和数值上均优于现有最先进的图像去雾方法。
