results: 实现4x4倍化超解像、三个设计的面镜和实时成像速度Abstract
Achieving both high-performance and wide field-of-view (FOV) super-resolution imaging has been attracting increasing attention in recent years. However, such goal suffers from long reconstruction time and huge storage space. Parallel compressive imaging (PCI) provides an efficient solution, but the super-resolution quality and imaging speed are strongly dependent on precise optical transfer function (OTF), modulation masks and reconstruction algorithm. In this work, we propose a wide FOV parallel compressive super-resolution imaging approach based on physics enhanced network. By training the network with the prior OTF of an arbitrary 128x128-pixel region and fine-tuning the network with other OTFs within rest regions of FOV, we realize both mask optimization and super-resolution imaging with up to 1020x1500 wide FOV. Numerical simulations and practical experiments demonstrate the effectiveness and superiority of the proposed approach. We achieve high-quality reconstruction with 4x4 times super-resolution enhancement using only three designed masks to reach real-time imaging speed. The proposed approach promotes the technology of rapid imaging for super-resolution and wide FOV, ranging from infrared to Terahertz.
摘要
实现高性能和广频谱场视野（FOV）超分辨成像已经引起了过去几年的关注。然而，这个目标受到了重建时间和存储空间的限制。并行压缩成像（PCI）提供了一个有效的解决方案，但是超分辨质量和成像速度受到了准确的光传导函数（OTF）、模拟面和重建算法的影响。在这项工作中，我们提议一种基于物理增强网络的广频谱场并行压缩超分辨成像方法。通过训练网络使用任意128x128像素区域的先前OTF，并在其他FOV区域中细化网络，我们实现了模拟面优化和超分辨成像，可达到1020x1500广频谱场。 numeral simulations and practical experiments demonstrate the effectiveness and superiority of the proposed approach. We achieve high-quality reconstruction with 4x4 times super-resolution enhancement using only three designed masks to reach real-time imaging speed. The proposed approach promotes the technology of rapid imaging for super-resolution and wide FOV, ranging from infrared to Terahertz.
An Invitation to Hypercomplex Phase Retrieval: Theory and Applications
paper_authors: Roman Jacome, Kumar Vijay Mishra, Brian M. Sadler, Henry Arguello
for: The paper is written for researchers and practitioners working in the field of hypercomplex signal processing (HSP) and its applications in optical imaging.
methods: The paper uses Clifford algebra to handle multidimensional signals and provides a synopsis of emerging areas and applications of hypercomplex phase retrieval (HPR) with a focus on optical imaging.
results: The paper discusses the opportunities for developing novel HSP tools and algorithms in the context of HPR, particularly in optical imaging applications.Abstract
Hypercomplex signal processing (HSP) provides state-of-the-art tools to handle multidimensional signals by harnessing intrinsic correlation of the signal dimensions through Clifford algebra. Recently, the hypercomplex representation of the phase retrieval (PR) problem, wherein a complex-valued signal is estimated through its intensity-only projections, has attracted significant interest. The hypercomplex PR (HPR) arises in many optical imaging and computational sensing applications that usually comprise quaternion and octonion-valued signals. Analogous to the traditional PR, measurements in HPR may involve complex, hypercomplex, Fourier, and other sensing matrices. This set of problems opens opportunities for developing novel HSP tools and algorithms. This article provides a synopsis of the emerging areas and applications of HPR with a focus on optical imaging.
摘要
超复杂信号处理（HSP）提供了当今最先进的工具来处理多维信号，通过CLIFFORD代数利用信号维度之间的自然相关性。在最近几年，使用超复杂表示法来解决频谱恢复（PR）问题，其中一个复数值信号通过其尺度仅的投影来估算，已经吸引了广泛的关注。这种超复杂PR（HPR）在光学成像和计算感知应用中广泛存在，通常包括四元数和八元数值信号。与传统PR问题相似，HPR问题中的测量可能包括复数、超复杂、傅里叶和其他感知矩阵。这些问题的出现为HSP工具和算法的开发提供了新的机会。本文将对HPR在光学成像领域的出现和应用进行简要的介绍。