A new image dehazing algorithm based on adaptive sky region is proposed in this paper, which shows good fidelity in sky region and satisfying visual effect in non-sky region. For robust sky segmentation, we propose a rough-to-fine method that can make a balance between efficiency and accuracy. Considering distribution of haze is inconsistent, we divide the input image into three parts and calculate their atmospheric lights respectively. To solve the problem of invalid dark channel prior, we make an improvement for the transmission estimation. Finally, image fusion is taken as a post processing that can solve the problem of partial darkness and ensure a visual pleasing result. The experimental results for both synthetic and natural hazy images demonstrate that our algorithm performs comparable or even better results than the state-of-the-art methods in terms of various measurement indexes, such as the PSNR, SSIM, and so forth. Besides, the proposed algorithm can be also applied in FPGA platform due to the optimized performance. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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使用自适应天空分割的单幅图像去雾

本文提出了一种基于自适应天空区域的图像去雾算法，该算法在天空区域表现出良好的逼真度，在非天空区域表现出令人满意的视觉效果。对于稳健的天空分割，我们提出了一种从粗到细的方法，可以在效率和准确性之间取得平衡。考虑到雾度分布不一致，我们将输入图像分为三部分，分别计算它们的大气光。为了解决暗通道先验无效的问题，我们对传输估计进行了改进。最后采用图像融合作为后处理，可以解决局部黑暗的问题，保证视觉效果。合成和自然模糊图像的实验结果表明，我们的算法在各种测量指标（如 PSNR、SSIM 等）方面的效果与最先进的方法相当甚至更好。此外，由于优化的性能，所提出的算法也可以应用于FPGA平台。© 2021 日本电气工程师学会。由 Wiley Periodicals LLC 出版。