融合特征增强及多尺度损失的增量去雾算法

Incremental Dehazing Algorithm Combining Feature Enhancement and Multi-Scale Loss

为提高雾霾图像清晰化效果并实现去雾能力的泛化,提出一种特征增强及多尺度损失约束的网络结构,并采用增量式训练方法对网络进行训练。网络由教师网络和学生网络构成。通过学习教师网络提取的标注样本注意力信息对学生网络提取的特征进行特征增强;将标注样本多尺度语义特征作为软标签,建立多尺度语义特征损失衡量机制,与全局像素差异损失级联,构建面向特征和像素的损失函数;采用增量式训练方法,教师网络为学生网络平衡不同数据集的新旧知识提供先验约束,使网络保留原有知识的前提下,快速提高对增补数据集的泛化能力。实验结果表明,所提算法在主观视觉效果与客观评价指标上均取得了较好的效果。

To improve the clarity of the haze image and realize the generalization of the dehazing ability, a network structure with feature enhancement and multi-scale loss constraint is proposed, which is trained by an incremental training method. The network consists of a teacher network and a student network. The student network enhances the features by learning the attention information of the labeled samples extracted by the teacher network and uses the multi-scale semantic features of the labeled samples as the soft targets. Besides, a multi-scale semantic feature loss measurement mechanism is established, which cascades with the global pixel difference loss to construct the loss function for feature and pixel levels. According to the incremental training method, the teacher network guides the student network to balance the relationship between the new and old knowledge of different datasets. Thus, the network can improve the generalization ability of the supplementary dataset quickly, while retains the original knowledge. Experiment results show that the proposed algorithm performs well in both subjective visual effects and objective evaluation indicators.