  In this paper, we introduce StreakNet-Arch, a novel signal processing
architecture designed for Underwater Carrier LiDAR-Radar (UCLR) imaging
systems, to address the limitations in scatter suppression and real-time
imaging. StreakNet-Arch formulates the signal processing as a real-time,
end-to-end binary classification task, enabling real-time image acquisition. To
achieve this, we leverage Self-Attention networks and propose a novel Double
Branch Cross Attention (DBC-Attention) mechanism that surpasses the performance
of traditional methods. Furthermore, we present a method for embedding
streak-tube camera images into attention networks, effectively acting as a
learned bandpass filter. To facilitate further research, we contribute a
publicly available streak-tube camera image dataset. The dataset contains
2,695,168 real-world underwater 3D point cloud data. These advancements
significantly improve UCLR capabilities, enhancing its performance and
applicability in underwater imaging tasks. The source code and dataset can be
found at https://github.com/BestAnHongjun/StreakNet .
