基于残差注意力和半监督学习的图像去雾算法

Image Dehazing Algorithm Based on Residual Attention and Semi-Supervised Learning

基于训练合成图像的去雾算法往往不能在真实图像数据集上取得较好效果。针对泛化能力不理想等问题,文中提出了一种基于残差注意力机制的半监督学习网络用于单幅图像去雾算法。其主干网络由编码器和解码器构成,通过使用堆叠的残差注意力模块调整不同尺度的特征权重,赋予重要特征更多权重。局部残差学习选择绕过薄雾区域,使模型关注有效信息。文中训练分为有监督学习和无监督学习两个分支,分别输入合成数据和真实数据,其中使用暗通道损失和全变分损失来约束无监督分支。实验结果表明,文中所提算法在合成数据集和真实数据集上均取得了较好的结果,图像的平均处理时间仅为0.01 s,在去雾效果和处理时间上实现了平衡。

Dehazing algorithms based on training synthetic images cannot achieve satisfactory results on real image data sets.In view of these problems of unsatisfactory generalization ability,this study proposes a semi-supervised learning network based on residual attention mechanism for single image dehaze.The backbone network of the proposed model consists of an encoder and a decoder.Using stacked residual attention modules,the feature weights of different scales are adjusted to give more weight to important features.Local residuals choose to bypass hazy regions so that the model can focus on valid information.The training in this study is divided into two branches:supervised learning and unsupervised learning,which input synthetic data and real data respectively.Dark channel loss and total variational loss are used to constrain the unsupervised branches.The results show that the proposed algorithm obtains ideal results on both synthetic data sets and real data sets,and the average processing time of images is only 0.01 s,achieving a balance between dehazing effect and processing time.