Synthetic Aperture Imaging With Events and Frames
The Event-based Synthetic Aperture Imaging (E-SAI) has recently been proposed to see through extremely dense occlusions. However, the performance of E-SAI is not consistent under sparse occlusions due to the dramatic decrease of signal events. This paper addresses this problem by leveraging the merits of both events and frames, leading to a fusion-based SAI (EF-SAI) that performs consistently under the different densities of occlusions. In particular, we first extract the feature from events and frames via multi-modal feature encoders and then apply a multi-stage fusion network for cross-modal enhancement and density-aware feature selection. Finally, a CNN decoder is employed to generate occlusion-free visual images from selected features. Extensive experiments show that our method effectively tackles varying densities of occlusions and achieves superior performance to the state-of-the-art SAI methods. Codes and datasets are available at https://github.com/smjsc/EF-SAI
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