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您的检索式：作者名="YAOMING BIAN"
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| 1 | Phase imaging with an untrained neural network显示文摘Most of the neural networks proposed so far for computational imaging(CI)in optics employ a supervised training strategy,and thus need a large training set to optimize their weights and biases.Setting aside the requirements of environmental and system stability during many hours of data acquisition,in many practical applications,it is unlikely to be possible to obtain sufficient numbers of ground-truth images for training.Here,we propose to overcome this limitation by incorporating into a conventional deep neural network a complete physical model that represents the process of image formation.The most significant advantage of the resulting physics-enhanced deep neural network(PhysenNet)is that it can be used without training beforehand,thus eliminating the need for tens of thousands of labeled data.We take single-beam phase imaging as an example for demonstration.We experimentally show that one needs only to feed PhysenNet a single diffraction pattern of a phase object,and it can automatically optimize the network and eventually produce the object phase through the interplay between the neural network and the physical model.This opens up a new paradigm of neural network design,in which the concept of incorporating a physical model into a neural network can be generalized to solve many other CI problems. | Fei Wang Yaoming Bian Haichao Wang Meng Lyu Giancarlo Pedrini Wolfgang Osten George Barbastathis Guohai Situ | 2020 | Light(Science & Applications)2020，9，1： | 3 |
| 2 | Earth Summit Mission 2022:Scientific Expedition and Research on Mt.Qomolangma Helps Reveal the Synergy between Westerly Winds and Monsoon and the Resulting Climatic and Environmental Effects显示文摘“Earth summit mission 2022”is one of the landmark scientific research activities of the Second Tibetan Plateau Scientific Expedition and Research(STEP).This scientific expedition firstly used advanced technology and methods to detect vertical meteorological elements and produce forecasts for mountain climbing.The“Earth summit mission 2022”Qomolangma scientific expedition exceeded an altitude of over 8000 meters for the first time and carried out a comprehensive scientific investigation mission on the summit of Mt.Qomolangma.Among the participants,the westerly–monsoon synergy and influence team stationed in the Mt.Qomolangma region had two tasks:1)detecting the vertical structure of the atmosphere for parameters such as wind,temperature,humidity,and pressure with advanced instruments for high-altitude detection at the Mt.Qomolangma base camp;and 2)observing extreme weather processes to ensure that members of the mountaineering team could successfully reach the top.Through this scientific expedition,a better understanding of the vertical structure and weather characteristics of the complex area of Mt.Qomolangma is gained. | Yaoming MA Weiqiang MA Huaguang DAI Lei ZHANG Fanglin SUN Jinqiang ZHANG Nan YAO Jianan HE Zhixuan BAI Yuejian XUAN Yunshuai ZHANG Yuan YUAN Chenyi YANG Weijun SUN Ping ZHAO Minghu DING Kongju ZHU Jie HU Bian Bazhuga Bai Juepingcuo Zhuo Ma Ren Qingnima Suo Langwangdui Yang Zong Haikun WEN | 2023 | Advances in Atmospheric Sciences2023，40，2： | 0 |
| 3 | Passive imaging through dense scattering media显示文摘Imaging through non-static and optically thick scattering media such as dense fog,heavy smoke,and turbid water is crucial in various applications.However,most existing methods rely on either active and coherent light illumination,or image priors,preventing their application in situations where only passive illumination is possible.In this study we present a universal passive method for imaging through dense scattering media that does not depend on any prior information.Combining the selection of small-angle components out of the incoming information-carrying scattering light and image enhancement algorithm that incorporates timedomain minimum filtering and denoising,we show that the proposed method can dramatically improve the signal-to-interference ratio and contrast of the raw camera image in outfield experiments. | YAOMING BIAN FEI WANG YUANZHE WANG ZHENFENG FU HAISHAN LIU HAIMING YUAN GUOHAI SITU | 2024 | Photonics Research2024，12，1： | 0 |
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