AFDN: ATTENTION-BASED FEEDBACK DEHAZING NETWORK FOR UAV REMOTE SENSING IMAGE HAZE REMOVAL

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.