Haze interferes the transmission of scene radiation and significantly degrades color and details of outdoor images. Existing deep neural networks-based image dehazing algorithms usually use some common networks. The network design does not model the image formation of haze process well, which accordingly leads to dehazed images containing artifacts and haze residuals in some special scenes. In this paper, we propose a task-oriented network for image dehazing, where the network design is motivated by the image formation of haze process. The task-oriented network involves a hybrid network containing an encoder and decoder network and a spatially variant recurrent neural network which is derived from the hazy process. In addition, we develop a multi-stage dehazing algorithm to further improve the accuracy by filtering haze residuals in a step-by-step fashion. To constrain the proposed network, we develop a dual composition loss, content-based pixel-wise loss and total variation constraint. We train the proposed network in an end-to-end manner and analyze its effect on image dehazing. Experimental results demonstrate that the proposed algorithm achieves favorable performance against state-of-the-art dehazing methods.

中文翻译：

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用于图像去雾的面向任务的网络。


雾霾会干扰场景辐射的传输，并显着降低户外图像的色彩和细节。现有的基于深度神经网络的图像去雾算法通常使用一些常见的网络。网络设计没有很好地模拟雾霾过程的图像形成，从而导致去雾图像在某些特殊场景中包含伪影和雾霾残留。在本文中，我们提出了一种面向任务的图像去雾网络，其中网络设计的动机是雾霾过程的图像形成。面向任务的网络涉及包含编码器和解码器网络的混合网络以及从模糊过程导出的空间变异循环神经网络。此外，我们开发了一种多级去雾算法，通过逐步过滤雾霾残差来进一步提高精度。为了约束所提出的网络，我们开发了双重组合损失、基于内容的像素损失和总变化约束。我们以端到端的方式训练所提出的网络并分析其对图像去雾的影响。实验结果表明，与最先进的去雾方法相比，所提出的算法取得了良好的性能。