Xingye Chen 1,2,3Chang Qiao 1,3,*Tao Jiang 4,5Jiahao Liu 4,6[ ... ]Jiamin Wu 1,3,***
Author Affiliations
Abstract
1 Department of Automation, Tsinghua University, Beijing 100084, China
2 Research Institute for Frontier Science, Beihang University, Beijing 100083, China
3 Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China
4 National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
5 College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
6 Laboratory of Image Processing and Intelligent Control of Ministry of Education of China, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Detection noise significantly degrades the quality of structured illumination microscopy (SIM) images, especially under low-light conditions. Although supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts, the requirement of a large amount of high-quality training data severely limits their applications. Here we developed a pixel-realignment-based self-supervised denoising framework for SIM (PRS-SIM) that trains an SIM image denoiser with only noisy data and substantially removes the reconstruction artifacts. We demonstrated that PRS-SIM generates artifact-free images with 20-fold less fluorescence than ordinary imaging conditions while achieving comparable super-resolution capability to the ground truth (GT). Moreover, we developed an easy-to-use plugin that enables both training and implementation of PRS-SIM for multimodal SIM platforms including 2D/3D and linear/nonlinear SIM. With PRS-SIM, we achieved long-term super-resolution live-cell imaging of various vulnerable bioprocesses, revealing the clustered distribution of Clathrin-coated pits and detailed interaction dynamics of multiple organelles and the cytoskeleton.
PhotoniX
2024, 5(1): 4
作者单位
摘要
1 华中科技大学材料科学与工程学院, 材料成形与模具技术国家重点实验室, 武汉 4300074
2 增材制造陶瓷材料教育部工程研究中心, 武汉 430074
云南具有丰富的矿产资源, 钠长石是当地的优势矿种之一, 然而目前钠长石产业结构单一, 产品附加值低。本文以云南钠长石为原材料, 采用数字光处理(DLP)技术制备钠长石陶瓷, 探究了烧结温度对钠长石陶瓷性能的影响, 并基于研究结果成功制备出结构复杂的钠长石陶瓷。结果表明, DLP技术制备的钠长石陶瓷成形效果良好, 钠长石陶瓷素坯的抗弯强度为18.30 MPa。随着烧结温度的升高, 钠长石陶瓷的主要物相为钠长石和钙长石, 无新相产生, 且钠长石陶瓷的收缩率、致密化程度和抗弯强度逐渐增大。当烧结温度达1 150 ℃时, 钠长石陶瓷试样收缩率最大, 为35.25%(Z方向), 微观组织致密, 力学性能良好, 其抗弯强度为98.69 MPa。将云南钠长石与数字光处理技术相结合制备高性能、结构复杂的钠长石陶瓷对促进云南钠长石的产业发展具有较为重要的推动作用。
钠长石 数字光处理 烧结温度 抗弯强度 微观形貌 收缩率 albite digital light processing sintering temperature bending strength microscopic morphology shrinkage 
硅酸盐通报
2023, 42(2): 736
Yi Zhang 1,2†Yuling Wang 1,2†Mingrui Wang 2,3,4,5Yuduo Guo 2,3,4[ ... ]Qionghai Dai 1,2,4,5,***
Author Affiliations
Abstract
1 Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
2 Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China
3 Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
4 Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China
5 IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
6 Hangzhou Zhuoxi Institute of Brain and Intelligence, Hangzhou, 311100, China
PhotoniX
2022, 3(1): 31
Yi Zhang 1,2†Yuling Wang 1,2†Mingrui Wang 2,3,4,5Yuduo Guo 2,3,4[ ... ]Qionghai Dai 1,2,4,5,***
Author Affiliations
Abstract
1 Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
2 Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China
3 Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
4 Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China
5 IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
6 Hangzhou Zhuoxi Institute of Brain and Intelligence, Hangzhou, 311100, China
High-speed visualization of three-dimensional (3D) processes across a large field of view with cellular resolution is essential for understanding living systems. Light-field microscopy (LFM) has emerged as a powerful tool for fast volumetric imaging. However, one inherent limitation of LFM is that the achievable lateral resolution degrades rapidly with the increase of the distance from the focal plane, which hinders the applications in observing thick samples. Here, we propose Spherical-Aberration-assisted scanning LFM (SAsLFM), a hardware-modification-free method that modulates the phase-space point-spread-functions (PSFs) to extend the effective high-resolution range along the z-axis by ~ 3 times. By transferring the foci to different depths, we take full advantage of the redundant light-field data to preserve finer details over an extended depth range and reduce artifacts near the original focal plane. Experiments on a USAF-resolution chart and zebrafish vasculatures were conducted to verify the effectiveness of the method. We further investigated the capability of SAsLFM in dynamic samples by imaging large-scale calcium transients in the mouse brain, tracking freely-moving jellyfish, and recording the development of Drosophila embryos. In addition, combined with deep-learning approaches, we accelerated the three-dimensional reconstruction of SAsLFM by three orders of magnitude. Our method is compatible with various phase-space imaging techniques without increasing system complexity and can facilitate high-speed large-scale volumetric imaging in thick samples.
PhotoniX
2022, 3(1): 30
孙冬 1,2陈双 1,2史玉升 1,2,*闫春泽 1,2[ ... ]文世峰 1,2
作者单位
摘要
1 华中科技大学材料科学与工程学院材料成形与模具技术国家重点实验室,湖北 武汉 430074
2 增材制造陶瓷材料教育部工程研究中心,湖北 武汉 430074
空心涡轮叶片是燃气轮机热端的关键零部件,其结构十分复杂,制造难度极大。陶瓷型芯型壳是用于涡轮叶片铸造的重要部件,随着燃气轮机的热端工作温度逐渐升高,所需要的叶片结构也更加精细,传统的精密铸造工艺已经无法满足叶片快速升级换代的需求。激光增材制造技术无需模具,可以加速新产品的研发,缩短制造周期,满足个性化需求。目前可用于型芯型壳制造的激光增材制造技术主要有激光选区烧结和立体光刻两大类。主要介绍了这两大类激光增材制造技术在陶瓷型芯型壳制备方面的应用,从材料配方、素坯成形、后处理烧结等多个方面综述了当前国内外的最新研究进展,探讨了两类技术各自的优势、目前存在的问题以及未来发展的趋势。
激光技术 增材制造 空心涡轮叶片 陶瓷型芯型壳 激光选区烧结 立体光刻 
中国激光
2022, 49(12): 1202002
李萌 1,2黄海露 1,2吴甲民 1,2,*刘春磊 1,2[ ... ]史玉升 1,2
作者单位
摘要
1 1.华中科技大学 材料科学与工程学院, 材料成形与模具技术国家重点实验室, 武汉430074
2 2.增材制造陶瓷材料教育部工程研究中心, 武汉430074
3 3.中国科学院 上海硅酸盐研究所, 高性能陶瓷和超微结构国家重点实验室, 上海200050
随着科技的不断发展, Si3N4陶瓷在航空、机械、生物医疗等高新领域发挥着越来越重要的作用。本工作采用包覆助烧剂Al2O3-Y2O3后的Si3N4粉体为原材料, 利用数字光处理(Digital light processing, DLP)技术成功制备出Si3N4陶瓷, 并系统研究了浆料固相含量对Si3N4陶瓷浆料、DLP成形Si3N4陶瓷素坯和陶瓷性能的影响。研究表明, 浆料固相含量低于40.0% (体积分数)时, 浆料在30 s-1剪切速率下的粘度均小于2 Pa·s, 可用于DLP成形。在这种情况下, 浆料的单层固化深度随浆料固相含量的增加而减小。随着浆料固相含量的增大, DLP成形Si3N4陶瓷的相对密度和抗弯强度先升高后降低。固相含量为37.5% (体积分数)的样品获得最大的相对密度和抗弯强度, 分别为89.8%和162.5 MPa, 较固相含量为32.5% (体积分数)的样品分别提升了10%和16%。本研究通过对陶瓷浆料性能的优化, 提升了DLP成形Si3N4陶瓷的性能, 为Si3N4等非氧化物陶瓷光固化成形奠定了实验基础。
氮化硅 数字光处理 固相含量 相对密度 抗弯强度 Si3N4 digital light processing solid loading relative density flexural strength 
无机材料学报
2021, 37(3): 310
曹继伟 1,2王沛 1,2刘志远 1,2刘长勇 1,2[ ... ]陈张伟 1,2,*
作者单位
摘要
1 1.深圳大学 增材制造研究所, 深圳 518060
2 2.广东省电磁控制与智能机器人重点实验室, 深圳 518060
3 3.华中科技大学 材料科学与工程学院, 材料成形与模具技术国家重点实验室, 武汉 430074
4 4.华中科技大学 增材制造陶瓷材料教育部工程研究中心, 武汉 430074
陶瓷以其优异的热物理化学性能在航空航天、能源、环保以及生物医疗等领域具有极大的应用潜力。随着这些领域相关技术的快速发展, 其核心零件部件外形结构设计日益复杂、内部组织逐步走向定制化、梯度化。陶瓷具有硬度高、脆性大等特点, 较难通过传统的加工成形方法实现异形结构零件的制造, 最终限制了陶瓷材料的工程应用范围。激光增材制造技术作为一种快速发展的增材制造技术, 在复杂精密陶瓷零部件的制造中具有显著优势: 无模、精度高、响应快以及周期短, 同时能够实现陶瓷零件组织结构灵活调配, 有望解决上述异形结构陶瓷零件成形问题。本文综述了多种基于粉末成形的激光增材制造陶瓷技术: 基于粉末床熔融的激光选区烧结和激光选区熔化; 基于定向能量沉积的激光近净成形技术。主要讨论了各类激光增材陶瓷技术的成形原理与特点, 综述了激光选区烧结技术中陶瓷坯体后处理致密化工艺以及激光选区熔化和激光近净成形技术这两种技术中所打印陶瓷坯体基体裂纹开裂行为分析及其控制方法的研究进展, 对比分析了激光选区烧结、激光选区熔化以及激光近净成形技术在成形陶瓷零件的技术特征, 最后展望了激光增材制造陶瓷技术的未来发展趋势。
激光增材制造 激光选区烧结 激光选区熔化 激光近净成形技术 陶瓷 综述 laser additive manufacturing selective laser sintering selective laser melting laser engineered net shaping ceramic review 
无机材料学报
2021, 37(3): 241
Tiankuang Zhou 1,2,3†Lu Fang 2,3†Tao Yan 1,2Jiamin Wu 1,2[ ... ]Qionghai Dai 1,2,6,8,*
Author Affiliations
Abstract
1 Department of Automation, Tsinghua University, Beijing 100084, China
2 Institute for Brain and Cognitive Science, Tsinghua University, Beijing 100084, China
3 Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
4 Beijing Innovation Center for Future Chip, Tsinghua University, Beijing 100084, China
5 Institute of Microelectronics, Tsinghua University, Beijing 100084, China
6 Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
7 e-mail: lin-x@tsinghua.edu.cn
8 e-mail: qhdai@tsinghua.edu.cn
This publisher’s note corrects the authors’ affiliations in Photon. Res.8, 940 (2020).PRHEIZ2327-912510.1364/PRJ.389553
Photonics Research
2020, 8(8): 08001323
Author Affiliations
Abstract
1 Peking University, College of Engineering, Department of Biomedical Engineering, Beijing, China
2 Southern University of Science and Technology China, Department of Biomedical Engineering, Shenzhen, Guangdong, China
3 Beijing Institute of Collaborative Innovation (BICI), Beijing, China
4 Tsinghua University, Department of Automation, Beijing, China
5 University of Technology Sydney, Faculty of Science, Institute for Biomedical Materials & Devices (IBMD), Ultimo, Australia
6 Peking University, School of Physics, Beijing, China
7 Peking University, School of Life Sciences, Biodynamic Optical Imaging Center (BIOPIC), Beijing, China
8 Peking University People’s Hospital Breast Center, Beijing, China
The pixel size of a charge-coupled device (CCD) camera plays a major role in the image resolution, and the square pixels are attributed to the physical anisotropy of the sampling frequency. We synthesize the high sampling frequency directions from multiple frames acquired with different angles to enhance the resolution by 1.4 × over conventional CCD orthogonal sampling. To directly demonstrate the improvement of frequency-domain diagonal extension (FDDE) microscopy, lens-free microscopy is used, as its resolution is dominantly determined by the pixel size. We demonstrate the resolution enhancement with a mouse skin histological specimen and a clinical blood smear sample. Further, FDDE is extended to lens-based photography with an ISO 12233 resolution target. This method paves a new way for enhancing the image resolution for a variety of imaging techniques in which the resolution is primarily limited by the sampling pixel size, for example, microscopy, photography, and spectroscopy.
frequency domain diagonal sampling super-resolution 
Advanced Photonics
2020, 2(3): 036005
Tiankuang Zhou 1,2,3†Lu Fang 2,3†Tao Yan 1,2Jiamin Wu 1,2[ ... ]Qionghai Dai 1,2,6,8,*
Author Affiliations
Abstract
1 Department of Automation, Tsinghua University, Beijing 100084, China
2 Institute for Brain and Cognitive Science, Tsinghua University, Beijing 100084, China
3 Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
4 Beijing Innovation Center for Future Chip, Tsinghua University, Beijing 100084, China
5 Institute of Microelectronics, Tsinghua University, Beijing 100084, China
6 Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
7 e-mail: lin-x@tsinghua.edu.cn
8 e-mail: qhdai@tsinghua.edu.cn
Training an artificial neural network with backpropagation algorithms to perform advanced machine learning tasks requires an extensive computational process. This paper proposes to implement the backpropagation algorithm optically for in situ training of both linear and nonlinear diffractive optical neural networks, which enables the acceleration of training speed and improvement in energy efficiency on core computing modules. We demonstrate that the gradient of a loss function with respect to the weights of diffractive layers can be accurately calculated by measuring the forward and backward propagated optical fields based on light reciprocity and phase conjunction principles. The diffractive modulation weights are updated by programming a high-speed spatial light modulator to minimize the error between prediction and target output and perform inference tasks at the speed of light. We numerically validate the effectiveness of our approach on simulated networks for various applications. The proposed in situ optical learning architecture achieves accuracy comparable to in silico training with an electronic computer on the tasks of object classification and matrix-vector multiplication, which further allows the diffractive optical neural network to adapt to system imperfections. Also, the self-adaptive property of our approach facilitates the novel application of the network for all-optical imaging through scattering media. The proposed approach paves the way for robust implementation of large-scale diffractive neural networks to perform distinctive tasks all-optically.
Photonics Research
2020, 8(6): 06000940

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