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Ghosting artifacts caused by moving objects or misalignments is a keychallenge in high dynamic range (HDR) imaging for dynamic scenes. Previousmethods first register the input low dynamic range (LDR) images using opticalflow before merging them, which are error-prone and cause ghosts in results. Avery recent work tries to bypass optical flows via a deep network withskip-connections, however, which still suffers from ghosting artifacts forsevere movement. To avoid the ghosting from the source, we propose a novelattention-guided end-to-end deep neural network (AHDRNet) to producehigh-quality ghost-free HDR images. Unlike previous methods directly stackingthe LDR images or features for merging, we use attention modules to guide themerging according to the reference image. The attention modules automaticallysuppress undesired components caused by misalignments and saturation andenhance desirable fine details in the non-reference images. In addition to theattention model, we use dilated residual dense block (DRDB) to make full use ofthe hierarchical features and increase the receptive field for hallucinatingthe missing details. The proposed AHDRNet is a non-flow-based method, which canalso avoid the artifacts generated by optical-flow estimation error.Experiments on different datasets show that the proposed AHDRNet can achievestate-of-the-art quantitative and qualitative results.

Attention-guided Network for Ghost-free High Dynamic Range Imaging