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 this https URL .

介绍了一种用于水下载波 LiDAR-Radar（UCLR）成像系统的新型信号处理架构 StreakNet-Arch，通过引入自注意力网络和提出的双分支交叉注意力机制（DBC-Attention），形成了实时的端到端二分类任务，实现了实时图像获取，并利用暗管摄像机图片嵌入到注意力网络中作为学习带通滤波器的方法，还提供了一个公开可用的暗管摄像机图片数据集，大大提高了 UCLR 的性能和在水下成像任务中的适用性。

StreakNet-Arch: 水下载波雷达成像的基于反散射网络的架构

StreakNet-Arch: An Anti-scattering Network-based Architecture for  Underwater Carrier LiDAR-Radar Imaging

Advancements in high-throughput biomedical applications necessitate
real-time, large field-of-view (FOV) imaging capabilities. Conventional
lens-free imaging (LFI) systems, while addressing the limitations of physical
lenses, have been constrained by dynamic, hard-to-model optical fields,
resulting in a limited one-shot FOV of approximately 20 $mm^2$. This
restriction has been a major bottleneck in applications like live-cell imaging
and automation of microfluidic systems for biomedical research. Here, we
present a deep-learning(DL)-based imaging framework -- GenLFI -- leveraging
generative artificial intelligence (AI) for holographic image reconstruction.
We demonstrate that GenLFI can achieve a real-time FOV over 550 $mm^2$,
surpassing the current LFI system by more than 20-fold, and even larger than
the world's largest confocal microscope by 1.76 times. The resolution is at the
sub-pixel level of 5.52 $\mu m$, without the need for a shifting light source.
The unsupervised learning-based reconstruction does not require optical field
modeling, making imaging dynamic 3D samples (e.g., droplet-based microfluidics
and 3D cell models) in complex optical fields possible. This GenLFI framework
unlocks the potential of LFI systems, offering a robust tool to tackle new
frontiers in high-throughput biomedical applications such as drug discovery.

GenLFI 是一种基于深度学习的图像重构框架，通过利用生成型人工智能，实现了超过 550 平方毫米的实时视场，解决了传统透镜无限制的困境，为生物医学研究中的高通量应用提供了强大的工具。