Three-dimensional (3-D) synthetic aperture radar (SAR) is widely used in many
security and industrial applications requiring high-resolution imaging of
concealed or occluded objects. The ability to resolve intricate 3-D targets is
essential to the performance of such applications and depends directly on
system bandwidth. However, because high-bandwidth systems face several
prohibitive hurdles, an alternative solution is to operate multiple radars at
distinct frequency bands and fuse the multiband signals. Current multiband
signal fusion methods assume a simple target model and a small number of point
reflectors, which is invalid for realistic security screening and industrial
imaging scenarios wherein the target model effectively consists of a large
number of reflectors. To the best of our knowledge, this study presents the
first use of deep learning for multiband signal fusion. The proposed network,
called kR-Net, employs a hybrid, dual-domain complex-valued convolutional
neural network (CV-CNN) to fuse multiband signals and impute the missing
samples in the frequency gaps between subbands. By exploiting the relationships
in both the wavenumber domain and wavenumber spectral domain, the proposed
framework overcomes the drawbacks of existing multiband imaging techniques for
realistic scenarios at a fraction of the computation time of existing multiband
fusion algorithms. Our method achieves high-resolution imaging of intricate
targets previously impossible using conventional techniques and enables finer
resolution capacity for concealed weapon detection and occluded object
classification using multiband signaling without requiring more advanced
hardware. Furthermore, a fully integrated multiband imaging system is developed
using commercially available millimeter-wave (mmWave) radars for efficient
multiband imaging.

提出一种深度学习方法 kR-Net，使用混合的双域复值卷积神经网络 (CV-CNN) 融合多频段信号，以实现对复杂目标的高分辨率成像，从而在安全筛查和工业成像领域中具有广阔应用前景。

基于深度学习的多波段信号融合用于三维 SAR 超分辨率

Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-Resolution

We present the first systematic study on concealed object detection (COD),
which aims to identify objects that are "perfectly" embedded in their
background. The high intrinsic similarities between the concealed objects and
their background make COD far more challenging than traditional object
detection/segmentation. To better understand this task, we collect a
large-scale dataset, called COD10K, which consists of 10,000 images covering
concealed objects in diverse real-world scenarios from 78 object categories.
Further, we provide rich annotations including object categories, object
boundaries, challenging attributes, object-level labels, and instance-level
annotations. Our COD10K is the largest COD dataset to date, with the richest
annotations, which enables comprehensive concealed object understanding and can
even be used to help progress several other vision tasks, such as detection,
segmentation, classification, etc. Motivated by how animals hunt in the wild,
we also design a simple but strong baseline for COD, termed the Search
Identification Network (SINet). Without any bells and whistles, SINet
outperforms 12 cutting-edge baselines on all datasets tested, making them
robust, general architectures that could serve as catalysts for future research
in COD. Finally, we provide some interesting findings and highlight several
potential applications and future directions. To spark research in this new
field, our code, dataset, and online demo are available on our project page:
this http URL

本研究提出了针对被隐藏物体的检测任务，提供了名为 COD10K 的大型数据集，涵盖 78 个物体类别的 10,000 个来自不同真实场景的图像，并提供了详细的注释。其中，Search Identification Network（SINet）是一种简单但强大的基线用于 COD，并在测试的所有数据集上击败了 12 个前沿基线。