分类： 光学 >> 量子光学 提交时间： 2023-02-19
Binbin Hong
Lei Sun
Wanlin Wang
Yanbing Qiu
Naixing Feng
Dong Su
Nutapong Somjit
Ian Robertson
Guo Ping Wang
摘要： The rapidly growing global data usage has demanded more efficient ways to utilize the scarce electromagnetic spectrum resource. Recent research has focused on the development of efficient multiplexing techniques in the millimeter-wave band (1-10 mm, or 30-300 GHz) due to the promise of large available bandwidth for future wireless networks. Frequency-division multiplexing is still one of the most commonly-used techniques to maximize the transmission capacity of a wireless network. Based on the frequency-selective tunnelling effect of the low-loss epsilon-near-zero metamaterial waveguide, we numerically and experimentally demonstrate five-channel frequency-division multiplexing and demultiplexing in the millimeter-wave range. We show that this device architecture offers great flexibility to manipulate the filter Q-factors and the transmission spectra of different channels, by changing of the epsilon-near-zero metamaterial waveguide topology and by adding a standard waveguide between two epsilon-near-zero channels. This strategy of frequency-division multiplexing may pave a way for efficiently allocating the spectrum for future communication networks.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
摘要： Semantic scene understanding with Minimalist Optical Systems (MOS) in mobile and wearable applications remains a challenge due to the corrupted imaging quality induced by optical aberrations. However, previous works only focus on improving the subjective imaging quality through computational optics, i.e. Computational Imaging (CI) technique, ignoring the feasibility in semantic segmentation. In this paper, we pioneer to investigate Semantic Segmentation under Optical Aberrations (SSOA) of MOS. To benchmark SSOA, we construct Virtual Prototype Lens (VPL) groups through optical simulation, generating Cityscapes-ab and KITTI-360-ab datasets under different behaviors and levels of aberrations. We look into SSOA via an unsupervised domain adaptation perspective to address the scarcity of labeled aberration data in real-world scenarios. Further, we propose Computational Imaging Assisted Domain Adaptation (CIADA) to leverage prior knowledge of CI for robust performance in SSOA. Based on our benchmark, we conduct experiments on the robustness of state-of-the-art segmenters against aberrations. In addition, extensive evaluations of possible solutions to SSOA reveal that CIADA achieves superior performance under all aberration distributions, paving the way for the applications of MOS in semantic scene understanding. Code and dataset will be made publicly available at https://github.com/zju-jiangqi/CIADA.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
摘要： Panoramic Annular Lens (PAL) composed of few lenses has great potential in panoramic surrounding sensing tasks for mobile and wearable devices because of its tiny size and large Field of View (FoV). However, the image quality of tiny-volume PAL confines to optical limit due to the lack of lenses for aberration correction. In this paper, we propose an Annular Computational Imaging (ACI) framework to break the optical limit of light-weight PAL design. To facilitate learning-based image restoration, we introduce a wave-based simulation pipeline for panoramic imaging and tackle the synthetic-to-real gap through multiple data distributions. The proposed pipeline can be easily adapted to any PAL with design parameters and is suitable for loose-tolerance designs. Furthermore, we design the Physics Informed Image Restoration Network (PI2RNet) considering the physical priors of panoramic imaging and single-pass physics-informed engine. At the dataset level, we create the DIVPano dataset and the extensive experiments on it illustrate that our proposed network sets the new state of the art in the panoramic image restoration under spatially-variant degradation. In addition, the evaluation of the proposed ACI on a simple PAL with only 3 spherical lenses reveals the delicate balance between high-quality panoramic imaging and compact design. To the best of our knowledge, we are the first to explore Computational Imaging (CI) in PAL. Code and datasets are publicly available at https://github.com/zju-jiangqi/ACI-PI2RNet.
点击量
待评议
点击量
下载量
评论
