Visual Attention Dehazing Network with Multi-level Features Refinement and Fusion

作者：

Highlights：

• The proposed network contains a feature extraction network, a recurrent refinement network and an encoder-decoder network.

• The recurrent refinement network generates and refines the haze attention map using low-level and high-level features as input alternatively.

• The encoder-decoder network predicts the dehazing result with the guidance of a haze attention map and the fused multi-level features.

摘要

•The proposed network contains a feature extraction network, a recurrent refinement network and an encoder-decoder network.•The recurrent refinement network generates and refines the haze attention map using low-level and high-level features as input alternatively.•The encoder-decoder network predicts the dehazing result with the guidance of a haze attention map and the fused multi-level features.

论文关键词：image dehazing,attention mechanism,multi-level features,recurrent network

论文评审过程：Received 24 July 2020, Revised 19 April 2021, Accepted 5 May 2021, Available online 16 May 2021, Version of Record 30 May 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108021