Image restoration under hazy weather condition, which is called single image dehazing, has been of significant interest for various computer vision applications. In recent years, deep learning-based methods have achieved success. However, existing image dehazing methods typically neglect the hierarchy of features in the neural network and fail to exploit their relationships fully. To this end, we propose an effective image dehazing method named Hierarchical Contrastive Dehazing (HCD), which is based on feature fusion and contrastive learning strategies. HCD consists of a hierarchical dehazing network (HDN) and a novel hierarchical contrastive loss (HCL). Specifically, the core design in the HDN is a Hierarchical Interaction Module, which utilizes multi-scale activation to revise the feature responses hierarchically. To cooperate with the training of HDN, we propose HCL which performs contrastive learning on hierarchically paired exemplars, facilitating haze removal. Extensive experiments on public datasets, RESIDE, HazeRD, and DENSE-HAZE, demonstrate that HCD quantitatively outperforms the state-of-the-art methods in terms of PSNR, SSIM and achieves better visual quality.
翻译:在寒冷的天气条件下恢复图像,即所谓的单一图像脱色,对于各种计算机视觉应用来说,人们一直非常感兴趣。近年来,深层次的学习方法取得了成功。但是,现有的图像脱色方法通常忽视神经网络特征的等级,没有充分利用它们之间的关系。为此,我们提议一种有效的图像脱色方法,名为 " 高层次相抗裂脱色法 " (HCD),它基于特征融合和对比学习战略。HCD由等级分解网络(HDN)和新的等级对比损失(HCL)组成。具体地说,HDN的核心设计是一个等级互动模块,它利用多级激活来修改特征反应。为了与HDN合作,我们提议HLL,它对等级配对齐的外层板进行对比性学习,便利清除烟雾。关于公共数据集、REIDE、HzeRDRD和DENSE-HAZE的大规模实验,表明HCD在数量上超越了SS质量和图像-MS的州级质量方法。