Improving the speed of ghost imaging is one of the main ways to leverage its advantages in sensitivity and imperfect spectral regions for practical applications.Because of the proportional relationship between image r...Improving the speed of ghost imaging is one of the main ways to leverage its advantages in sensitivity and imperfect spectral regions for practical applications.Because of the proportional relationship between image resolution and measurement time,when the image pixels are large,the measurement time increases,making it difficult to achieve real-time imaging.Therefore,a high-quality ghost imaging method based on undersampled natural-order Hadamard is proposed.This method uses the characteristics of the Hadamard matrix under undersampling conditions where image information can be fully obtained but overlaps,as well as deep learning to extract aliasing information from the overlapping results to obtain the true original image information.We conducted numerical simulations and experimental tests on binary and grayscale objects under undersampling conditions to demonstrate the effectiveness and scalability of this method.This method can significantly reduce the number of measurements required to obtain high-quality image information and advance application promotion.展开更多
We take phase modulation to create discrete phase-controlled sources and realize the super-bunching effect by a phasecorrelated method. From theoretical and numerical simulations, we find the space translation invaria...We take phase modulation to create discrete phase-controlled sources and realize the super-bunching effect by a phasecorrelated method. From theoretical and numerical simulations, we find the space translation invariance of the bunching effect is a key point for the ghost imaging realization. Experimentally, we create the orderly phase-correlated discrete sources which can realize high-visibility second-order ghost imaging than the result with chaotic sources. Moreover, some factors affecting the visibility of ghost image are discussed in detail.展开更多
In the existing ghost-imaging-based cryptographic key distribution(GCKD)protocols,the cryptographic keys need to be encoded by using many modulated patterns,which undoubtedly incurs long measurement time and huge memo...In the existing ghost-imaging-based cryptographic key distribution(GCKD)protocols,the cryptographic keys need to be encoded by using many modulated patterns,which undoubtedly incurs long measurement time and huge memory consumption.Given this,based on snapshot compressive ghost imaging,a public network cryptographic key distribution protocol is proposed,where the cryptographic keys and joint authentication information are encrypted into several color block diagrams to guarantee security.It transforms the previous single-pixel sequential multiple measurements into multi-pixel single exposure measurements,significantly reducing sampling time and memory storage.Both simulation and experimental results demonstrate the feasibility of this protocol and its ability to detect illegal attacks.Therefore,it takes GCKD a big step closer to practical applications.展开更多
We report an experimental demonstration of temporal ghost imaging in which a digital micromirror device(DMD)and+1/-1 binary modulation have been combined to give an accurate reconstruction of a nonperiodic time object...We report an experimental demonstration of temporal ghost imaging in which a digital micromirror device(DMD)and+1/-1 binary modulation have been combined to give an accurate reconstruction of a nonperiodic time object.Compared to the 0/1 modulation,the reconstruction signal can be improved greatly by+1/-1 binary modulation even with half of the measurements.Experimental results show that 0/1 binary temporal objects up to 4 kHz and sinusoidal time objects up to 1 kHz can be reconstructed by this method.The influences of modulation speed and array detector gray levels are also discussed.展开更多
为了降低服装目标检测模型的参数量和浮点型计算量,提出一种改进的轻量级服装目标检测模型——GYOLOv5s.首先使用Ghost卷积重构YOLOv5s的主干网络;然后使用DeepFashion2数据集中的部分数据进行模型训练和验证;最后将训练好的模型用于服...为了降低服装目标检测模型的参数量和浮点型计算量,提出一种改进的轻量级服装目标检测模型——GYOLOv5s.首先使用Ghost卷积重构YOLOv5s的主干网络;然后使用DeepFashion2数据集中的部分数据进行模型训练和验证;最后将训练好的模型用于服装图像的目标检测.实验结果表明,G-YOLOv5s的mAP达到71.7%,模型体积为9.09 MB,浮点型计算量为9.8 G FLOPs,与改进前的YOLOv5s网络相比,模型体积压缩了34.8%,计算量减少了41.3%,精度仅下降1.3%,方便部署在资源有限的设备中使用.展开更多
基金the Science and Technology Development Plan Project of Jilin Province,China(Grant No.20220204134YY)the National Natural Science Foundation of China(Grant No.62301140)+3 种基金Project of the Education Department of Jilin Province(Grant Nos.JJKH20231292KJ and JJKH20240242KJ)Program for Science and Technology Development of Changchun City(Grant No.23YQ11)Innovation and Entrepreneurship Talent Funding Project of Jilin Province(Grant No.2023RY17)the Project of Jilin Provincial Development and Reform Commission(Grant No.2023C042-4).
文摘Improving the speed of ghost imaging is one of the main ways to leverage its advantages in sensitivity and imperfect spectral regions for practical applications.Because of the proportional relationship between image resolution and measurement time,when the image pixels are large,the measurement time increases,making it difficult to achieve real-time imaging.Therefore,a high-quality ghost imaging method based on undersampled natural-order Hadamard is proposed.This method uses the characteristics of the Hadamard matrix under undersampling conditions where image information can be fully obtained but overlaps,as well as deep learning to extract aliasing information from the overlapping results to obtain the true original image information.We conducted numerical simulations and experimental tests on binary and grayscale objects under undersampling conditions to demonstrate the effectiveness and scalability of this method.This method can significantly reduce the number of measurements required to obtain high-quality image information and advance application promotion.
基金Project supported by the National Natural Science Foundation of China(Grant No.62105188)。
文摘We take phase modulation to create discrete phase-controlled sources and realize the super-bunching effect by a phasecorrelated method. From theoretical and numerical simulations, we find the space translation invariance of the bunching effect is a key point for the ghost imaging realization. Experimentally, we create the orderly phase-correlated discrete sources which can realize high-visibility second-order ghost imaging than the result with chaotic sources. Moreover, some factors affecting the visibility of ghost image are discussed in detail.
基金supported by the Beijing Natural Science Foundation(Grant No.4222016).
文摘In the existing ghost-imaging-based cryptographic key distribution(GCKD)protocols,the cryptographic keys need to be encoded by using many modulated patterns,which undoubtedly incurs long measurement time and huge memory consumption.Given this,based on snapshot compressive ghost imaging,a public network cryptographic key distribution protocol is proposed,where the cryptographic keys and joint authentication information are encrypted into several color block diagrams to guarantee security.It transforms the previous single-pixel sequential multiple measurements into multi-pixel single exposure measurements,significantly reducing sampling time and memory storage.Both simulation and experimental results demonstrate the feasibility of this protocol and its ability to detect illegal attacks.Therefore,it takes GCKD a big step closer to practical applications.
基金Project supported by Beijing Institute of Technology Research Fund Program for Young Scholars(Grant No.202122012).
文摘We report an experimental demonstration of temporal ghost imaging in which a digital micromirror device(DMD)and+1/-1 binary modulation have been combined to give an accurate reconstruction of a nonperiodic time object.Compared to the 0/1 modulation,the reconstruction signal can be improved greatly by+1/-1 binary modulation even with half of the measurements.Experimental results show that 0/1 binary temporal objects up to 4 kHz and sinusoidal time objects up to 1 kHz can be reconstructed by this method.The influences of modulation speed and array detector gray levels are also discussed.
文摘为了降低服装目标检测模型的参数量和浮点型计算量,提出一种改进的轻量级服装目标检测模型——GYOLOv5s.首先使用Ghost卷积重构YOLOv5s的主干网络;然后使用DeepFashion2数据集中的部分数据进行模型训练和验证;最后将训练好的模型用于服装图像的目标检测.实验结果表明,G-YOLOv5s的mAP达到71.7%,模型体积为9.09 MB,浮点型计算量为9.8 G FLOPs,与改进前的YOLOv5s网络相比,模型体积压缩了34.8%,计算量减少了41.3%,精度仅下降1.3%,方便部署在资源有限的设备中使用.
