当前位置: X-MOL 学术IEEE Signal Process. Lett. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Multiscale Supervision-Guided Context Aggregation Network for Single Image Dehazing
IEEE Signal Processing Letters ( IF 3.9 ) Pub Date : 2021-11-13 , DOI: 10.1109/lsp.2021.3125272
Nian Wang , Zhigao Cui , Yanzhao Su , Chuan He , Aihua Li

End-to-end learning-based image dehazing methods tend to overdehaze or underdehaze in real scenes due to inefficient feature extraction and feature fusion. In this letter, we propose a multiscale supervision-guided context aggregation network (MSGCAN) based on two principles: improving feature extraction and enhancing feature mapping. To improve feature extraction, an attention-guided context aggregation (AGCA) module is adopted to merge context features extracted by several residual dense blocks (RDB). Moreover, we output these aggregated context features on each scale and form multiscale supervision to enhance feature mapping and ensure that the extracted features on each scale contain more realistic details. The experimental results show that the proposed MSGCAN performs better than other state-of-the-art dehazing methods in both synthetic and real-world scenes.

中文翻译:

用于单幅图像去雾的多尺度监督引导的上下文聚合网络

由于特征提取和特征融合效率低下,基于端到端学习的图像去雾方法在真实场景中往往会出现过度去雾或去雾不足的问题。在这封信中,我们提出了一个基于两个原则的多尺度监督引导上下文聚合网络(MSGCAN):改进特征提取和增强特征映射。为了改进特征提取,采用注意力引导的上下文聚合(AGCA)模块来合并由几个残差密集块(RDB)提取的上下文特征。此外,我们在每个尺度上输出这些聚合的上下文特征并形成多尺度监督以增强特征映射并确保每个尺度上提取的特征包含更真实的细节。
更新日期:2021-11-13
down
wechat
bug