paper_authors: Paul Balondrade, Victor Barolle, Nicolas Guigui, Emeric Auriant, Nathan Rougier, Claude Boccara, Mathias Fink, Alexandre Aubry
for: 实现深入、实时、量化的生物组织观察
methods: 多спектル矩阵方法
results: 实现0.1mm^3的场视野，290nm的分辨率，1Hz的帧率三维图像Abstract
Label-free microscopy exploits light scattering to obtain a three-dimensional image of biological tissues. However, light propagation is affected by aberrations and multiple scattering, which drastically degrade the image quality and limit the penetration depth. Multi-conjugate adaptive optics and time-gated matrix approaches have been developed to compensate for aberrations but the associated frame rate is extremely limited for 3D imaging. Here we develop a multi-spectral matrix approach to solve these fundamental problems. Based on an interferometric measurement of a polychromatic reflection matrix, the focusing process can be optimized in post-processing at any voxel by addressing independently each frequency component of the wave-field. A proof-of-concept experiment demonstrates the three-dimensional image of an opaque human cornea over a 0.1 mm^3-field-of-view at a 290 nm-resolution and a 1 Hz-frame rate. This work paves the way towards a fully-digital microscope allowing real-time, in-vivo, quantitative and deep inspection of tissues.
摘要
Label-free microscopy 利用光散射获取生物组织的三维图像。然而，光束传播受到偏振和多散射的影响，导致图像质量严重下降，限制了温度深度。多 conjugate adaptive optics 和时间锁定矩阵方法已经开发，但这些方法的相关帧率非常低，不适于3D图像。在这种情况下，我们开发了一种多 spectral matrix 方法。基于一种多色干涉测量，我们可以在后处理中独立地处理每个频率成分的波场，从而优化焦点处理。一个证明实验表明，我们可以在0.1 mm^3 的场视野内获得290 nm 的分辨率和1 Hz 的帧率。这种工作开创了一种完全数字的镜像机，允许实时、生物体内、量化和深入检查组织。
paper_authors: Grace Kuo, Florian Schiffers, Douglas Lanman, Oliver Cossairt, Nathan Matsuda for: Multisource holography is proposed as a novel architecture to suppress speckle in a single frame without sacrificing resolution.methods: The approach uses an array of sources, two spatial light modulators, and an algorithm to calculate multisource holograms.results: The proposed method can achieve up to a 10 dB increase in peak signal-to-noise ratio compared to an equivalent single source system, and is validated through a benchtop experimental prototype.Abstract
Holographic displays promise several benefits including high quality 3D imagery, accurate accommodation cues, and compact form-factors. However, holography relies on coherent illumination which can create undesirable speckle noise in the final image. Although smooth phase holograms can be speckle-free, their non-uniform eyebox makes them impractical, and speckle mitigation with partially coherent sources also reduces resolution. Averaging sequential frames for speckle reduction requires high speed modulators and consumes temporal bandwidth that may be needed elsewhere in the system. In this work, we propose multisource holography, a novel architecture that uses an array of sources to suppress speckle in a single frame without sacrificing resolution. By using two spatial light modulators, arranged sequentially, each source in the array can be controlled almost independently to create a version of the target content with different speckle. Speckle is then suppressed when the contributions from the multiple sources are averaged at the image plane. We introduce an algorithm to calculate multisource holograms, analyze the design space, and demonstrate up to a 10 dB increase in peak signal-to-noise ratio compared to an equivalent single source system. Finally, we validate the concept with a benchtop experimental prototype by producing both 2D images and focal stacks with natural defocus cues.
摘要
激光显示技术承诺了许多优点，包括高质量3D图像、准确的视力缩放和具有减小的形态因素。然而，激光学依赖于 coherent 照明，可能会在最终图像中产生不жела的斑点噪声。尽管平滑相位激光可以无斑点，但它们的非均匀观看窗口使其实际无法应用，而使用半共振源也会降低分辨率。均值多帧图像以提高噪声抑制效果需要高速调制器，这会占用时间频谱资源，这些资源可能需要在系统中用于其他目的。在这项工作中，我们提出了多源激光学技术，一种新的架构，使用一个数组源来抑制斑点。通过使用两个空间光模ulator，其中每个源在数组中可以被控制为创建不同的斑点版本，并且在图像平面上均值多个源的贡献可以抑制斑点。我们提出了一种算法来计算多源激光图，分析设计空间，并证明在相同的单个源系统中，我们可以获得最高达10 dB的峰值信号响应比。最后，我们验证了这一概念，使用了一个桌面实验prototype，并生成了2D图像和自然减ocus图像。
InSPECtor: an end-to-end design framework for compressive pixelated hyperspectral instruments
results: 研究人员通过使用这种方法，可以减少数据量，采集时间和探测器空间，并且不会产生重要的信息损失。实际上，这种方法可以减少数据量的40倍，相比于传统的高spectral仪器。Abstract
Classic designs of hyperspectral instrumentation densely sample the spatial and spectral information of the scene of interest. Data may be compressed after the acquisition. In this paper we introduce a framework for the design of an optimized, micro-patterned snapshot hyperspectral imager that acquires an optimized subset of the spatial and spectral information in the scene. The data is thereby compressed already at the sensor level, but can be restored to the full hyperspectral data cube by the jointly optimized reconstructor. This framework is implemented with TensorFlow and makes use of its automatic differentiation for the joint optimization of the layout of the micro-patterned filter array as well as the reconstructor. We explore the achievable compression ratio for different numbers of filter passbands, number of scanning frames, and filter layouts using data collected by the Hyperscout instrument. We show resulting instrument designs that take snapshot measurements without losing significant information while reducing the data volume, acquisition time, or detector space by a factor of 40 as compared to classic, dense sampling. The joint optimization of a compressive hyperspectral imager design and the accompanying reconstructor provides an avenue to substantially reduce the data volume from hyperspectral imagers.
摘要
We evaluate the achievable compression ratio for various filter passband numbers, scanning frame numbers, and filter layouts using data from the Hyperscout instrument. Our results show that the proposed instrument designs can capture snapshot measurements without losing significant information, while reducing the data volume, acquisition time, and detector space by a factor of 40 compared to traditional, dense sampling. The joint optimization of the compressive hyperspectral imager design and the accompanying reconstructor provides a means to significantly reduce the data volume from hyperspectral imagers.
Minimum-length chain embedding for the phase unwrapping problem on D-Wave’s advantage architecture
paper_authors: Mohammad Kashfi Haghighi, Nikitas Dimopoulos
for: 解决 phase unwrapping 问题
methods: 使用 quantum annealing 和 Pegasus 图的嵌入
results: 提出一种新的嵌入算法，可以更好地解决 phase unwrapping 问题，并且可以应用于其他问题的嵌入Abstract
With the current progress of quantum computing, quantum annealing is being introduced as a powerful method to solve hard computational problems. In this paper, we study the potential capability of quantum annealing in solving the phase unwrapping problem, an instance of hard computational problems. To solve the phase unwrapping problem using quantum annealing, we deploy the D-Wave Advantage machine which is currently the largest available quantum annealer. The structure of this machine, however, is not compatible with our problem graph structure. Consequently, the problem graph needs to be mapped onto the target (Pegasus) graph, and this embedding significantly affects the quality of the results. Based on our experiment and also D-Wave's reports, the lower chain lengths can result in a better performance of quantum annealing. In this paper, we propose a new embedding algorithm that has the lowest possible chain length for embedding the graph of the phase unwrapping problem onto the Pegasus graph. The obtained results using this embedding strongly outperform the results of Auto-embedding provided by D-Wave. Besides the phase unwrapping problem, this embedding can be used to embed any subset of our problem graph to the Pegasus graph.
摘要
现在量子计算技术的进步，量子热处理已经被提出为解决复杂计算问题的强大方法。在这篇论文中，我们研究了量子热处理可以解决阶跃问题，这是复杂计算问题的一个实例。为解决阶跃问题使用量子热处理，我们使用D-Wave Advantage机器，该机器目前是最大的量子热处理器。然而，这台机器的结构与我们的问题图结构不兼容，因此需要将问题图映射到目标（ Pegasus）图上，这种映射会对结果产生深见影响。根据我们的实验和D-Wave的报告，较短的链长可以使量子热处理表现更好。在这篇论文中，我们提出了一种新的映射算法，该算法可以将问题图映射到 Pegasus 图上，并且 obtenains 最低的链长。使用这种映射，我们在实验中获得了较好的结果，比D-Wave 提供的 Auto-embedding 更好。此外，这种映射可以将任何我们问题图的子集映射到 Pegasus 图上。
Disentangled Information Bottleneck guided Privacy-Protective JSCC for Image Transmission
paper_authors: Lunan Sun, Yang Yang, Mingzhe Chen, Caili Guo
For: 这个研究旨在保护私人资讯，同时确保传输过程中的通信效率。* Methods: 我们提出了一个混合资源和通道编码（JSCC）方法，并将其与私人资讯分离的方法（DIB）搭配，以实现高效且安全的传输。* Results: 我们的方法可以将私人资讯与公共资讯分离，并且可以实现高品质的传输。实验结果显示，我们的方法可以降低窃听者对私人资讯的准确率，并且可以降低传输时间。Abstract
Joint source and channel coding (JSCC) has attracted increasing attention due to its robustness and high efficiency. However, JSCC is vulnerable to privacy leakage due to the high relevance between the source image and channel input. In this paper, we propose a disentangled information bottleneck guided privacy-protective JSCC (DIB-PPJSCC) for image transmission, which aims at protecting private information as well as achieving superior communication performance at the legitimate receiver. In particular, we propose a DIB objective to disentangle private and public information. The goal is to compress the private information in the public subcodewords, preserve the private information in the private subcodewords and improve the reconstruction quality simultaneously. In order to optimize JSCC neural networks using the DIB objective, we derive a differentiable estimation of the DIB objective based on the variational approximation and the density-ratio trick. Additionally, we design a password-based privacy-protective (PP) algorithm which can be jointly optimized with JSCC neural networks to encrypt the private subcodewords. Specifically, we employ a private information encryptor to encrypt the private subcodewords before transmission, and a corresponding decryptor to recover the private information at the legitimate receiver. A loss function for jointly training the encryptor, decryptor and JSCC decoder is derived based on the maximum entropy principle, which aims at maximizing the eavesdropping uncertainty as well as improving the reconstruction quality. Experimental results show that DIB-PPJSCC can reduce the eavesdropping accuracy on private information up to $15\%$ and reduce $10\%$ inference time compared to existing privacy-protective JSCC and traditional separate methods.
摘要
joint source和通道编码（JSCC）已经吸引了越来越多的关注，因为它具有高效率和鲁棒性。然而，JSCC受到隐私泄露的威胁，因为源图像和通道输入之间存在高度相关性。在本文中，我们提出了一种基于信息瓶颈的隐私保护JSCC（DIB-PPJSCC），用于图像传输，以保护私人信息并实现合法接收器的超越性表现。具体来说，我们提出了一个DIB目标，用于分离私人信息和公共信息。我们的目标是压缩私人信息在公共子码字中，保持私人信息在私人子码字中，并同时提高重建质量。为了优化JSCC神经网络使用DIB目标，我们 deriv了一个可导的DIB目标基于变量 aproximation和density-ratio trick。此外，我们设计了一种基于密码的隐私保护算法（PP），可以与JSCC神经网络 jointly 优化，以加密私人子码字。具体来说，我们使用一个私人信息加密器加密私人子码字，并在合法接收器中使用相应的解密器恢复私人信息。我们 derive了基于最大 entropy 原理的损失函数，用于同时优化加密器、解密器和JSCC解码器的训练。实验结果表明，DIB-PPJSCC可以降低私人信息泄露率达15%，并提高重建质量10%，相比之下存在隐私保护JSCC和传统分离方法。