Single image dehazing is a challenging ill-posed restoration problem. Various prior-based and learning-based methods have been proposed. Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium. The formulation of haze in realistic environment is more complicated. In this paper, we propose to take its essential mechanism as "black box", and focus on learning an input-adaptive trainable end-to-end dehazing model. An U-Net like encoder-decoder deep network via progressive feature fusions has been proposed to directly learn highly nonlinear transformation function from observed hazy image to haze-free ground-truth. The proposed network is evaluated on two public image dehazing benchmarks. The experiments demonstrate that it can achieve superior performance when compared with popular state-of-the-art methods. With efficient GPU memory usage, it can satisfactorily recover ultra high definition hazed image up to 4K resolution, which is unaffordable by many deep learning based dehazing algorithms.

本文提出了一个基于U-Net的编码器-解码器深层网络模型用于单幅图像去雾。该模型通过逐步特征融合，直接从观察到的有雾图像到去雾地面真实的高度非线性变换函数。在两个公共的图像去雾基准测试中，该模型在GPU的高效内存使用下能够令人满意地恢复超高清分辨率模糊图像，达到了顶尖水平。

逐层特征融合网络用于逼真图像去雾