Removing haze from real-world images is challenging due to unpredictable weather conditions, resulting in misaligned hazy and clear image pairs. In this paper, we propose a non-aligned supervision framework that consists of three networks - dehazing, airlight, and transmission. In particular, we explore a non-alignment setting by utilizing a clear reference image that is not aligned with the hazy input image to supervise the dehazing network through a multi-scale reference loss that compares the features of the two images. Our setting makes it easier to collect hazy/clear image pairs in real-world environments, even under conditions of misalignment and shift views. To demonstrate this, we have created a new hazy dataset called "Phone-Hazy", which was captured using mobile phones in both rural and urban areas. Additionally, we present a mean and variance self-attention network to model the infinite airlight using dark channel prior as position guidance, and employ a channel attention network to estimate the three-channel transmission. Experimental results show that our framework outperforms current state-of-the-art methods in the real-world image dehazing. Phone-Hazy and code will be available at https://github.com/hello2377/NSDNet.

翻译：从真实世界图像中去除雾霾具有挑战性，因为不可预测的天气条件会导致有雾和清晰图像对的不对齐。在本文中，我们提出了一种非对齐监督框架，该框架由三个网络组成——去雾网络、大气光网络和透射率网络。具体而言，我们探索了一种非对齐设置，通过利用与有雾输入图像不对齐的清晰参考图像，借助多尺度参考损失比较两幅图像的特征来监督去雾网络。我们的设置使得在真实环境中更容易收集有雾/清晰图像对，即使在对齐偏移和视角变化条件下也是如此。为验证这一点，我们创建了一个名为"Phone-Hazy"的新雾霾数据集，该数据集通过手机在乡村和城市区域拍摄。此外，我们提出了一种均值和方差自注意力网络，利用暗通道先验作为位置引导来建模无限大气光，并采用通道注意力网络估计三通道透射率。实验结果表明，我们的框架在真实世界图像去雾中优于当前最先进的方法。Phone-Hazy数据集和代码将发布于https://github.com/hello2377/NSDNet。