Images captured in a hazy environment usually suffer from bad visibility and missing information. Over many years, learning-based and handcrafted prior-based dehazing algorithms have been rigorously developed. However, both algorithms exhibit some weaknesses in terms of haze removal performance. Therefore, in this work, we have proposed the patch-map-based hybrid learning DehazeNet, which integrates these two strategies by using a hybrid learning technique involving the patch map and a bi-attentive generative adversarial network. In this method, the reasons limiting the performance of the dark channel prior (DCP) have been analyzed. A new feature called the patch map has been defined for selecting the patch size adaptively. Using this map, the limitations of the DCP (e.g., color distortion and failure to recover images involving white scenes) can be addressed efficiently. In addition, to further enhance the performance of the method for haze removal, a patch-map-based DCP has been embedded into the network, and this module has been trained with the atmospheric light generator, patch map selection module, and refined module simultaneously. A combination of traditional and learning-based methods can efficiently improve the haze removal performance of the network. Experimental results show that the proposed method can achieve better reconstruction results compared to other state-of-the-art haze removal algorithms.

中文翻译：

---

PMHLD：基于补丁图的混合学习DehazeNet，用于单图像雾度去除

在朦胧环境中捕获的图像通常会遭受可见性差和信息丢失的困扰。多年来，已经严格开发了基于学习和手工制作的基于先验的除雾算法。但是，这两种算法在除雾性能方面都表现出一些弱点。因此，在这项工作中，我们提出了基于补丁图的混合学习DehazeNet，它通过使用涉及补丁图和双注意力生成对抗网络的混合学习技术，将这两种策略整合在一起。在这种方法中，已经分析了限制暗通道先验（DCP）性能的原因。已经定义了一个称为补丁图的新功能，用于自适应地选择补丁大小。使用此图，可以了解DCP的局限性（例如，色彩失真和无法恢复涉及白色场景的图像）可以得到有效解决。此外，为了进一步提高除雾方法的性能，基于补丁图的DCP已嵌入到网络中，并且该模块已与大气光发生器，补丁图选择模块和改进模块同时进行了培训。传统方法和基于学习的方法的组合可以有效地提高网络的除雾性能。实验结果表明，与其他最新的除雾算法相比，该方法可以实现更好的重建效果。并且该模块已与大气光发生器，斑块图选择模块和精炼模块同时进行了培训。传统方法和基于学习的方法的组合可以有效地提高网络的除雾性能。实验结果表明，与其他最新的除雾算法相比，该方法可以实现更好的重建效果。并且该模块已与大气光发生器，斑块图选择模块和精炼模块同时进行了培训。传统方法和基于学习的方法的组合可以有效地提高网络的除雾性能。实验结果表明，与其他最新的除雾算法相比，该方法可以实现更好的重建效果。