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| 1 | Ways to sparse representation:An overview显示文摘Many algorithms have been proposed to find sparse representations over redundant dictionaries or transforms. This paper gives an overview of these algorithms by classifying them into three categories:greedy pursuit algorithms,lp norm regularization based algorithms,and iterative shrinkage algorithms. We summarize their pros and cons as well as their connections. Based on recent evidence,we conclude that the algorithms of the three categories share the same root:lp norm regularized inverse problem. Finally,several topics that deserve further investigation are also discussed. | YANG JingYu PENG YiGang XU WenLi DAI QiongHai | 2009 | Science in China(Series F)2009，52，4： | 15 |
| 2 | In situ optical backpropagation training of diffractive optical neural networks显示文摘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 networlks,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 dlassification 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 difractive neural networks to perform distinctive tasks all-optically. | TIANKUANG ZHOU LU FANG TAO YAN JIAMIN WU YIPENG LI JINGTAO FAN HUAQIANG WU XING LIN QIONGHAI DA | 2020 | Photonics Research2020，8，6： | 11 |
| 3 | An overview of computational photography显示文摘Computational photography is an emerging multidisciplinary field.Over the last two decades,it has integrated studies across computer vision,computer graphics,signal processing,applied optics and related disciplines.Researchers are exploring new ways to break through the limitations of traditional digital imaging for the benefit of photographers,vision and graphics researchers,and image processing programmers.Thanks to much effort in various associated fields,the large variety of issues related to these new methods of photography are described and discussed extensively in this paper.To give the reader the full picture of the voluminous literature related to computational photography,this paper briefly reviews the wide range of topics in this new field,covering a number of different aspects,including:(i) the various elements of computational imaging systems and new sampling and reconstruction mechanisms;(ii) the different image properties which benefit from computational photography,e.g.depth of field,dynamic range;and(iii) the sampling subspaces of visual scenes in the real world.Based on this systematic review of the previous and ongoing work in this field,we also discuss some open issues and potential new directions in computational photography.This paper aims to help the reader get to know this new field,including its history,ultimate goals,hot topics,research methodologies,and future directions,and thus build a foundation for further research and related developments. | SUO JinLi JI XiangYang DAI QiongHai | 2012 | Science China(Information Sciences)2012，55，6： | 9 |
| 4 | Broadband perovskite quantum dot spectrometer beyond human visual resolution显示文摘The quantum dot spectrometer,fabricated by integrating different quantum dots with an image sensor to reconstruct the target spectrum from spectral-coupled measurements,is an emerging and promising hyperspectrometry technology with high resolution and a compact size.The spectral resolution and spectral range of quantum dot spectrometers have been limited by the spectral variety of the available quantum dots and the robustness of algorithmic reconstruction.Moreover,the spectrometer integration of quantum dots also suffers from inherent photoluminescence emission and poor batch-to-batch repeatability.In this work,we developed nonemissive in situ fabricated MA_(3)Bi_(2)X_(9) and Cs_(2)SnX_(6)(MA=CH_(3)NH_(3);X=Cl,Br,I)perovskite-quantum-dot-embedded films(PQDFs)with precisely tunable transmittance spectra for quantum dot spectrometer applications.The resulting PQDFs contain in situ fabricated perovskite nanocrystals with homogenous dispersion in a polymeric matrix,giving them advantageous features such as high transmittance efficiency and good batch-to-batch repeatability.By integrating a filter array of 361 kinds of PQDFs with a silicon-based photodetector array,we successfully demonstrated the construction of a perovskite quantum dot spectrometer combined with a compressive-sensing-based total-variation optimization algorithm.A spectral resolution of ~1.6 nm was achieved in the broadband of 250-1000 nm.The performance of the perovskite quantum dot spectrometer is well beyond that of human eyes in terms of both the spectral range and spectral resolution.This advancement will not only pave the way for using quantum dot spectrometers for practical applications but also significantly impact the development of artificial intelligence products,clinical treatment equipment,scientific instruments,etc. | Xiaoxiu Zhu Liheng Bian Hao Fu Lingxue Wang Bingsuo Zou Qionghai Dai Jun Zhang Haizheng Zhong | 2020 | Light(Science & Applications)2020，9，1： | 5 |
| 5 | An Overlay Multicast Routing Algorithm Based on Genetic Algorithms显示文摘 | CHENG Peng DAI Qionghai WU Qiufeng | 2007 | Chinese Journal of Electronics2007，16，1： | 4 |
| 6 | From Brain Science to Artificial Intelligence显示文摘Reviewing the history of the development of artificial intelligence(AI)clearly reveals that brain science has resulted in breakthroughs in AI,such as deep learning.At present,although the developmental trend in AI and its applications has surpassed expectations,an insurmountable gap remains between AI and human intelligence.It is urgent to establish a bridge between brain science and AI research,including a link from brain science to AI,and a connection from knowing the brain to simulating the brain.The first steps toward this goal are to explore the secrets of brain science by studying new brain-imaging technology;to establish a dynamic connection diagram of the brain;and to integrate neuroscience experiments with theory,models,and statistics.Based on these steps,a new generation of AI theory and methods can be studied,and a subversive model and working mode from machine perception and learning to machine thinking and decision-making can be established.This article discusses the opportunities and challenges of adapting brain science to AI. | Jingtao Fan Lu Fang Jiamin Wu Yuchen Guo Qionghai Dai | 2020 | Engineering2020，6，3： | 4 |
| 7 | Analog Optical Computing for Artificial Intelligence显示文摘The rapid development of artificial intelligence(AI)facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data.Optical computing provides a distinctive perspective to address this bottleneck by harnessing the unique properties of photons including broad bandwidth,low latency,and high energy efficiency.In this review,we introduce the latest developments of optical computing for different AI models,including feedforward neural networks,reservoir computing,and spiking neural networks(SNNs).Recent progress in integrated photonic devices,combined with the rise of AI,provides a great opportunity for the renaissance of optical computing in practical applications.This effort requires multidisciplinary efforts from a broad community.This review provides an overview of the state-of-the-art accomplishments in recent years,discusses the availability of current technologies,and points out various remaining challenges in different aspects to push the frontier.We anticipate that the era of large-scale integrated photonics processors will soon arrive for practical AI applications in the form of hybrid optoelectronic frameworks. | Jiamin Wu Xing Lin Yuchen Guo Junwei Liu Lu Fang Shuming Jiao Qionghai Dai | 2022 | Engineering2022，8，3： | 4 |
| 8 | A Wide Base Line Multiple Camera System for High Performance 3D Video and Free Viewpoint Video显示文摘 | LIU Yebin DAI Qionghai XU Wenli | 2009 | Chinese Journal of Electronics2009，18，2： | 3 |
| 9 | DiLFM:an artifact-suppressed and noise-robust light-field microscopy through dictionary learning显示文摘Light field microscopy(LFM)has been widely used for recording 3D biological dynamics at camera frame rate.However,LFM suffers from artifact contaminations due to the illness of the reconstruction problem via naive Richardson-Lucy(RL)deconvolution.Moreover,the performance of LFM significantly dropped in low-light conditions due to the absence of sample priors.In this paper,we thoroughly analyze different kinds of artifacts and present a new LFM technique termed dictionary LFM(DiLFM)that substantially suppresses various kinds of reconstruction artifacts and improves the noise robustness with an over-complete dictionary.We demonstrate artifact-suppressed reconstructions in scattering samples such as Drosophila embryos and brains.Furthermore,we show our DiLFM can achieve robust blood cell counting in noisy conditions by imaging blood cell dynamic at 100 Hz and unveil more neurons in whole-brain calcium recording of zebrafish with low illumination power in vivo. | Yuanlong Zhang Bo Xiong Yi Zhang Zhi Lu Jiamin Wu Qionghai Dai | 2021 | Light(Science & Applications)2021，10，8： | 2 |
| 10 | Key technologies of light field capture for 3D reconstruction in microscopic scene显示文摘Light field capture for 3D reconstruction in microscopic scene is a very promising and useful technology, which can be extensively applied to life sciences, medicine, materials science, etc. This paper summarizes the key technologies in the evolution of microscopes for capturing 3D information, including wavefrontreconstruction, holography, fluorescence, tomography and so on. To give in-depth insights into them, detailed analyses and comparisons are provided. Finally, some future potential work in terms of light field capture and its application are discussed at length. | WANG Yu, JI XiangYang & DAI QiongHai Broadband Networks & Digital Media Lab of Automation Department, Tsinghua University, Beijing 100084, China | 2010 | Science China(Information Sciences)2010，53，10： | 2 |
| 11 | Recent Advances in Computational Photography显示文摘Traditional photography focuses on the optimization of lenses for a perfect imaging system.However, with the great developments of computational resources and optical modulation devices, we can achieve more powerful imaging abilities with concise optics.Computational photography is such an emerging interdisciplinary field by incorporating computational strategy in traditional imaging system to break the limitations in various dimensions such as spectrum, time and space. Recent advances in different aspects have aroused great interests and introduced tremendous applications in biology, material science and computer vision. | DAI Qionghai WU Jiamin FAN Jingtao XU Feng CAO Xun | 2019 | Chinese Journal of Electronics2019，28，1： | 2 |
| 12 | A fast algorithm for computing multidimensional DCT on certain small sizes 显示文摘 | Chen Xinjian Dai Qionghai Li Chunwen | 2003 | IEEE Trans on Signal Processing2003，51，1： | 1 |
| 13 | Mirror-enhanced scanning light-field microscopy for long-term high-speed 3D imaging with isotropic resolution显示文摘Various biological behaviors can only be observed in 3D at high speed over the long term with low phototoxicity.Light-field microscopy(LFM)provides an elegant compact solution to record 3D information in a tomographic manner simultaneously,which can facilitate high photon efficiency.However,LFM still suffers from the missing-cone problem,leading to degraded axial resolution and ringing effects after deconvolution.Here,we propose a mirrorenhanced scanning LFM(MiSLFM)to achieve long-term high-speed 3D imaging at super-resolved axial resolution with a single objective,by fully exploiting the extended depth of field of LFM with a tilted mirror placed below samples.To establish the unique capabilities of MiSLFM,we performed extensive experiments,we observed various organelle interactions and intercellular interactions in different types of photosensitive cells under extremely low light conditions.Moreover,we demonstrated that superior axial resolution facilitates more robust blood cell tracking in zebrafish larvae at high speed. | Bo Xiong Tianyi Zhu Yuhan Xiang Xiaopeng Li Jinqiang Yu Zheng Jiang Yihan Niu Dong Jiang Xu Zhang Lu Fang Jiamin Wu Qionghai Dai | 2021 | Light(Science & Applications)2021，10，12： | 1 |
| 14 | Unsupervised content-preserving transformation for optical microscopy显示文摘The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation,which is gradually changing the landscape of optical imaging and biomedical research.However,current implementations of deep learning usually operate in a supervised manner,and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability.Here,we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy,even in some cases in which supervised models cannot be applied.Through the introduction of a saliency constraint,the unsupervised model,named Unsupervised content-preserving Transformation for Optical Microscopy(UTOM);can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content.UTOM shows promising performance in a wide range of biomedical image transformation tasks,including in silico histological staining,fluorescence image restoration,and virtual fluorescence labeling.Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities.We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging. | Xinyang Li Guoxun Zhang Hui Qiao Feng Bao Yue Deng Jiamin Wu Yangfan He Jingping Yun Xing Lin Hao Xie Haoqian Wang Qionghai Dai | 2021 | Light(Science & Applications)2021，10，3： | 1 |
| 15 | In situ optical backpropagation training of diffractive optical neural networks:publisher’s note显示文摘This publisher’s note corrects the authors’affiliations in Photon.Res.8,940(2020). | TIANKUANG ZHOU LU FANG TAO YAN JIAMIN WU YIPENG LI JINGTAO FAN HUAQIANG WU XING LIN QIONGHAI DAI | 2020 | Photonics Research2020，8，8： | 1 |
| 16 | Three-dimensionalmotion estimation via matrix completion 显示文摘 | Li Kun Dai Qionghai Xu Wenli | 2012 | IEEE Trans onSystems Man and Cybernetics Part B: Cybernetics2012，42，2： | 1 |
| 17 | Histogram mining based on markov chain and its application to image categorization 显示文摘 | LI Fei DAI Qionghai XU Wenli | 2007 | Signal Processing: Image Communication2007，，9： | 1 |
| 18 | Hybrid fusion and interpolation algorithm with near-infrared image显示文摘 | Xiaoyan LUO Jun ZHANG Qionghai DAI | 2015 | Frontiers of Computer Science2015，9，3： | 1 |
| 19 | A fast algorithm for computing multidimensional DCT on certain small sizes显示文摘 | Chen Xinjian Dai Qionghai Li Chunwen | 2003 | IEEE Trans on Signal Processing2003，51，1： | 1 |
| 20 | Fast adaptive wavelet packets using interscale embedding of decomposition structures 显示文摘 | Yang Jingyu Xu Wenli l)ai Qionghai | 2010 | Pattern Recognition Letters2010，31，： | 1 |
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