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：从真实世界中去除图像雾霾具有挑战性，因为不可预测的天气条件会导致有雾图像与清晰图像对之间出现错位。本文提出一个由去雾网络、大气光网络和透射率网络三个子网络组成的非对齐监督框架。具体而言，我们探索了一种非对齐设置，利用与有雾输入图像不对齐的清晰参考图像，通过多尺度参考损失比较两幅图像的特征来监督去雾网络。该设置使得在真实环境下（即使存在错位和视角偏移）收集有雾/清晰图像对变得更加容易。为验证这一方法，我们创建了一个名为"Phone-Hazy"的新雾霾数据集，该数据集使用手机在城乡区域采集。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，我们的框架在真实图像去雾任务上优于当前最先进方法。Phone-Hazy数据集和代码将在https://github.com/hello2377/NSDNet 公开。