Single image dehazing is a critical image pre-processing step for many practical vision systems. Most existing dehazing methods solve this problem utilizing various of hand-crafted priors or by supervised training on the synthetic hazy image information (such as haze-free image, transmission map and atmospheric light). However, the assumptions on the hand-crafted priors are easily violated and collecting realistic transmission map and atmospheric light are unpractical. In this paper, we propose a novel weakly supervised network based on the multi-level multi-scale block. The proposed network reduces the constraint on the training data and automatically estimates the transmission map and the atmospheric light as well as the intermediate haze-free image without using any realistic transmission map and atmospheric light as supervision. Moreover, the estimated intermediate haze-free image helps to generate accurate transmission map and atmospheric light by embedding the physical-model, which presents reliable restoration of the final haze-free image. In particular, our network also can be trained on the real-world dataset to fine-tune the model and the fine-tuning operation improves the dehazing performance on the real-world dataset. Quantitative and qualitative experimental results demonstrate the proposed method performs on par with the supervised methods.

对于许多实际的视觉系统而言，单图像去雾是关键的图像预处理步骤。大多数现有的除雾方法利用各种手工制作的先验图像或通过对合成模糊图像信息（例如无雾图像，透射图和大气光）的有监督训练来解决该问题。但是，关于手工制作先验的假设很容易被违反，并且收集现实的透射图和大气光是不切实际的。在本文中，我们提出了一种基于多级多尺度块的新型弱监督网络。提出的网络减少了对训练数据的约束，并自动估计了透射图和大气光以及中间无雾图像，而无需使用任何实际的透射图和大气光作为监督。此外，估计的中间无雾图像通过嵌入物理模型有助于生成准确的透射图和大气光，从而可以可靠地还原最终的无雾图像。尤其是，我们的网络也可以在真实世界的数据集上进行训练，以对模型进行微调，并且微调操作可以提高真实世界数据集的除雾性能。定量和定性的实验结果表明，该方法的性能与监督方法相当。我们的网络还可以在真实世界的数据集上进行训练，以对模型进行微调，并且微调操作可以提高真实世界数据集的除雾性能。定量和定性的实验结果表明，该方法的性能与监督方法相当。我们的网络还可以在真实数据集上进行训练，以对模型进行微调，并且微调操作可以提高真实数据集的除雾性能。定量和定性的实验结果表明，该方法的性能与监督方法相当。