Feature Aggregation Attention Network for Single Image Dehazing
文献类型:会议论文
作者 | Yan, Lan3,4; Zheng, Wenbo2,4; Gou, Chao1; Wang, Fei-Yue4 |
出版日期 | 2020-10-20 |
会议日期 | 2020-10-20 |
会议地点 | United Arab Emirates |
英文摘要 | Due to its ill-posed nature, single image dehazing is a challenging problem. In this paper, we propose an end-to-end feature aggregation attention network (FAAN) for single image dehazing. It incorporates the idea of attention mechanism and residual learning and can adaptively aggregate different level features. In particular, in the proposed FANN, we design a novel block structure consisting of feature attention module, smoothed dilated convolution and local residual learning. The local residual learning allows the less useful information to be bypassed through multiple skip connections. The feature attention module is designed to assign more weight to important features. The smoothed dilated convolution is adopted to enlarge the receptive field without the negative influence of gridding artifacts. The experiments on the RESIDE dataset show that the proposed approach acquires state-of-the-art performance in both qualitative and quantitative measures. |
语种 | 英语 |
源URL | [http://ir.ia.ac.cn/handle/173211/48879] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
作者单位 | 1.School of Intelligent Systems Engineering, Sun Yat-sen University 2.School of Software Engineering, Xi'an Jiaotong University 3.University of Chinese Academy of Sciences 4.State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Yan, Lan,Zheng, Wenbo,Gou, Chao,et al. Feature Aggregation Attention Network for Single Image Dehazing[C]. 见:. United Arab Emirates. 2020-10-20. |
入库方式: OAI收割
来源:自动化研究所
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