Aiming at the existing single image haze removal algorithms, which are based on prior knowledge and assumptions, subject to many limitations in practical applications, and could suffer from noise and halo amplification. An end-to-end system is proposed in this paper to reduce defects by combining the prior knowledge and deep learning method. The haze image is decomposed into the base layer and detail layers through a weighted guided image filter (WGIF) firstly, and the airlight is estimated from the base layer. Then, the base layer image is passed to the efficient deep convolutional network for estimating the transmission map. To restore object close to the camera completely without amplifying noise in sky or heavily hazy scene, an adaptive strategy is proposed based on the value of the transmission map. If the transmission map of a pixel is small, the base layer of the haze image is used to recover a haze-free image via atmospheric scattering model, finally. Otherwise, the haze image is used. Experiments show that the proposed method achieves superior performance over existing methods.
翻译:瞄准基于先前知识和假设的现有单一图像烟雾清除算法,这些算法以先前的知识和假设为基础,在实际应用中有许多限制,并可能受到噪音和光圈放大的影响。本文件提议了一个端对端系统,通过将先前的知识和深层学习方法结合起来,减少缺陷。烟雾图像首先通过加权制导图像过滤器分解成基层和细层,从基层对空气光进行估计。然后,基层图像传递到高效的深层相控网络,用于估计传输图。为了完全恢复接近相机的物体,而不在天空或严重模糊的场景中放大噪音,根据传输图的价值提出了适应性战略。如果像素的传输图小,则使用烟雾图像的基础层,通过大气散射模型恢复无烟雾图像。最后,使用了雾图像。实验表明,拟议的方法比现有方法效果优。