基于双层视觉及多尺度注意力融合的图像去雾

Image Dehazing via Double-layer Vision and Multi-scale Attention Fusion

针对现有去雾算法缺乏对雾霾图像不同区域噪音浓度的关注以及远近景特征的区分问题,本文提出了一种新的生成对抗网络模型.模型中通过两个UNet3+网络实现全尺度的跳跃连接和深度监督,使用多尺度融合的方法结合不同尺度特征图中的高低级语义;而深度监督的加入可以更好地学习图像中的远近层次表示.同时在生成器结构中加入融合改进自注意力机制的多尺度金字塔特征融合模块,以便更好地保留特征图的多尺度结构信息,并且提高了对不同雾霾浓度区域的关注度.实验结果显示,在NTIRE 2020、NTIRE 2021、O-Haze数据集和Dense-Haze数据集上,本文所提出的算法网络相比BPPNET等其他先进算法可以得到更好的视觉效果,在Dense-Haze数据集上,峰值信噪比和结构相似性指数分别达到24.82和0.769.

Aiming at the problem that the existing dehazing algorithms lack attention to the noise concentration in different regions of the hazy image and the distinction between far and near features, this paper proposes a new generative adversarial network model. In the model, two UNet3 + networks are used to realize the full-scale jump connection and depth supervision, and multi-scale feature fusion is used to extract the high and low-level semantics in different scale feature images. The addition of deep supervision can better learn the near-far level representation in the image. At the same time, the multi-scale pyramid feature fusion module integrating the self-attention mechanism is added to the generator structure to better retain the multi-scale structure information of the feature map and improve the attention to different haze concentration regions. The experimental results show that the algorithm network can obtain better visual effects than other advanced algorithms such as BPPNET on NTIRE 2020, NTIRE 2021, O-Haze datasets, and Dense-Haze datasets. The peak signal-to-noise ratio and structural similarity index on the Dense-Haze dataset are, respectively, 24.82 and 0.769.