2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-RSDA.3
Paper Title AFDN: ATTENTION-BASED FEEDBACK DEHAZING NETWORK FOR UAV REMOTE SENSING IMAGE HAZE REMOVAL
Authors Shan Wang, Hanlin Wu, Libao Zhang, Beijing Normal University, China
SessionSS-RSDA: Special Session: Computer Vision and Machine Learning for Remote Sensing Data Analysis
LocationArea C
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Special Sessions: Computer Vision and Machine Learning for Remote Sensing Data Analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract To efficiently remove haze in unmanned aerial vehicle (UAV) remote sensing images, a novel attention-based feedback dehazing network (AFDN) is proposed, which is constructed by feedback connections and attention-based feedback blocks (AFBs). It has three major advantages compared with other dehazing algorithms: 1) The feedback connections, which allow network to use previous state to improve current performance, can effectively help the proposed AFDN generate clear remote sensing scenes progressively. 2) The AFBs are specially designed to extract global residual features, in which the dual attention block can usefully reduce redundant information and improve the fitting ability of network. 3) To obtain abundant texture information from UAV remote sensing images and restore real ground surfaces, an energy loss is employed for texture features learning. Experiments on synthetic datasets and real UAV remote sensing images verify the superiority of AFDN over several state-of-the-art methods in terms of qualitative and quantitative analysis.