results: 实验表明，这种方法可以 Correctly retrieve the complex field associated with darkfield measurements, and also analytically retrieve complex aberrations of an imaging system with no additional hardware. Compared to traditional FPM method, APIC method is more robust against aberrations and can achieve higher resolution.Abstract
Computational imaging methods empower modern microscopy with the ability of producing high-resolution, large field-of-view, aberration-free images. One of the dominant computational label-free imaging methods, Fourier ptychographic microscopy (FPM), effectively increases the spatial-bandwidth product of conventional microscopy by using multiple tilted illuminations to achieve high-throughput imaging. However, its iterative reconstruction method is prone to parameter selection, can be computationally expensive and tends to fail under excessive aberrations. Recently, spatial Kramers-Kronig methods show it is possible to analytically reconstruct complex field but lacks the ability of correcting aberrations or providing extended resolution enhancement. Here, we present a closed-form method, termed APIC, which weds the strengths of both methods. A new analytical phase retrieval framework is established in APIC, which demonstrates, for the first time, the feasibility of analytically reconstructing the complex field associated with darkfield measurements. In addition, APIC can analytically retrieve complex aberrations of an imaging system with no additional hardware. By avoiding iterative algorithms, APIC requires no human designed convergence metric and always obtains a closed-form complex field solution. The faithfulness and correctness of APIC's reconstruction are guaranteed due to its analytical nature. We experimentally demonstrate that APIC gives correct reconstruction result while FPM fails to do so when constrained to the same number of measurements. Meanwhile, APIC achieves 2.8 times faster computation using image tile size of 256 (length-wise). We also demonstrate APIC is unprecedentedly robust against aberrations compared to FPM - APIC is capable of addressing aberration whose maximal phase difference exceeds 3.8${\pi}$ when using a NA 0.25 objective in experiment.
摘要
计算成像技术为现代微镜技术提供了高分辨率、大场视野、抗偏振的图像生成能力。其中一种主导的计算无标签成像方法是傅立叶探针微镜（FPM），它通过多个倾斜照明来实现高通过率成像。然而，它的迭代重建方法容易选择参数、计算成本较高并且容易在过度偏振下失败。最近，空间克劳斯-克朗根方法表明了可以分析地重建复杂场的可能性，但它缺乏修正偏振或提供扩展的分辨率提升能力。我们现在介绍一种关闭式方法，称之为APIC，它将两种方法的优点相结合。我们建立了一个新的分析phaserecovery框架，可以分析地重建相关的黑场测量中的复杂场。此外，APIC可以分析地重建抗偏振系统的复杂偏振。通过避免迭代算法，APIC不需要人类设计的整合度量，总是得到关闭式的复杂场解决方案。由于APIC的分析性质，它的重建 faithfulness和正确性得到保证。我们实验表明，在相同数量的测量下，APIC可以正确地重建图像，而FPM则失败。此外，APIC在图像分割大小为256（长wise）时计算速度为2.8倍。我们还证明APIC对偏振强度的Robustness比FPM更高，可以处理偏振 whose maximal phase difference exceeds 3.8$\pi$ when using a NA 0.25 objective in experiment.
Deep Joint Source-Channel Coding for Adaptive Image Transmission over MIMO Channels
For: 该论文提出了一种基于视力变换器（ViT）的深度联合源和渠道编码（DeepJSCC）方案，用于无线图像传输。* Methods: 该方案使用了自注意力机制，以智能学习特征映射和功率分配策略，适应源图像和存在的通道条件。* Results: 数值实验表明， DeepJSCC-MIMO 可以在开loop和关loop MIMO 系统中提高传输质量，并且具有鲁棒性和灵活性，不需要重新训练。Abstract
This paper introduces a vision transformer (ViT)-based deep joint source and channel coding (DeepJSCC) scheme for wireless image transmission over multiple-input multiple-output (MIMO) channels, denoted as DeepJSCC-MIMO. We consider DeepJSCC-MIMO for adaptive image transmission in both open-loop and closed-loop MIMO systems. The novel DeepJSCC-MIMO architecture surpasses the classical separation-based benchmarks with robustness to channel estimation errors and showcases remarkable flexibility in adapting to diverse channel conditions and antenna numbers without requiring retraining. Specifically, by harnessing the self-attention mechanism of ViT, DeepJSCC-MIMO intelligently learns feature mapping and power allocation strategies tailored to the unique characteristics of the source image and prevailing channel conditions. Extensive numerical experiments validate the significant improvements in transmission quality achieved by DeepJSCC-MIMO for both open-loop and closed-loop MIMO systems across a wide range of scenarios. Moreover, DeepJSCC-MIMO exhibits robustness to varying channel conditions, channel estimation errors, and different antenna numbers, making it an appealing solution for emerging semantic communication systems.
摘要
The novel DeepJSCC-MIMO architecture surpasses traditional separation-based benchmarks and is robust to channel estimation errors. It also demonstrates great flexibility in adapting to diverse channel conditions and antenna numbers without requiring retraining.The DeepJSCC-MIMO scheme utilizes the self-attention mechanism of ViT to intelligently learn feature mapping and power allocation strategies tailored to the unique characteristics of the source image and the prevailing channel conditions. This leads to significant improvements in transmission quality for both open-loop and closed-loop MIMO systems across a wide range of scenarios.Moreover, DeepJSCC-MIMO exhibits robustness to varying channel conditions, channel estimation errors, and different antenna numbers, making it a promising solution for emerging semantic communication systems. Extensive numerical experiments validate the effectiveness of the proposed scheme.
Learning the Imaging Model of Speed-of-Sound Reconstruction via a Convolutional Formulation
results: 对于 simulate数据和实验室数据，使用这种方法可以提高SoS成像的对比度，比传统手动设计的模型更高。在人体实验中，使用这种方法可以提高SoS成像的对比度7倍和10倍。Abstract
Speed-of-sound (SoS) is an emerging ultrasound contrast modality, where pulse-echo techniques using conventional transducers offer multiple benefits. For estimating tissue SoS distributions, spatial domain reconstruction from relative speckle shifts between different beamforming sequences is a promising approach. This operates based on a forward model that relates the sought local values of SoS to observed speckle shifts, for which the associated image reconstruction inverse problem is solved. The reconstruction accuracy thus highly depends on the hand-crafted forward imaging model. In this work, we propose to learn the SoS imaging model based on data. We introduce a convolutional formulation of the pulse-echo SoS imaging problem such that the entire field-of-view requires a single unified kernel, the learning of which is then tractable and robust. We present least-squares estimation of such convolutional kernel, which can further be constrained and regularized for numerical stability. In experiments, we show that a forward model learned from k-Wave simulations improves the median contrast of SoS reconstructions by 63%, compared to a conventional hand-crafted line-based wave-path model. This simulation-learned model generalizes successfully to acquired phantom data, nearly doubling the SoS contrast compared to the conventional hand-crafted alternative. We demonstrate equipment-specific and small-data regime feasibility by learning a forward model from a single phantom image, where our learned model quadruples the SoS contrast compared to the conventional hand-crafted model. On in-vivo data, the simulation- and phantom-learned models respectively exhibit impressive 7 and 10 folds contrast improvements over the conventional model.
摘要
声速（SoS）是一种emergingultrasound contrast模式，使用普通探测器的推送-回声技术可以获得多种优点。为了估计组织声速分布，使用不同探测器序列的相对速度异常可以获得很好的结果。这种方法基于一个前向模型，该模型关系 soughtlocal声速值与观察到的速度异常，并解决了相关的图像重建问题。图像重建精度因此具有手工设计的前向图像模型的依赖性。在这种工作中，我们提出了学习SoS图像模型的方法。我们将探测器序列的pulse-echo SoS图像问题转换为一个 convolutional 形式，使得整个场景需要一个单一的核心，可以通过学习来解决。我们介绍了 least-squares 估算这个 convolutional 核心，可以进一步约束和减少数据稳定性。在实验中，我们发现使用k-WaveSimulation学习的前向模型可以提高SoS重建的 median 对比度by 63%，比 convential hand-crafted line-based wave-path模型更好。这个simulation-learned模型在实际数据上成功地泛化，对于acquired phantom data，它可以nearly double SoS对比度。我们还证明了设备特定和小数据 режим的可行性，通过学习一个 forward model从single phantom image中学习。在in vivo数据上，simulation-和 phantom-learned模型分别展示出了很出色的7和10倍对比度提高。