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公开(公告)号：US12051211B2
公开(公告)日：2024-07-30
申请号：US17989453
申请日：2022-11-17
Applicant: University of Electronic Science and Technology of China , Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China
Inventor： Jiang Qian , Haitao Lyu , Junzheng Jiang , Minfeng Xing
CPC classification number: G06T7/20 , G06T2207/20081
Abstract: A moving target focusing method and system based on a generative adversarial network are provided. The method includes: generating, using a Range Doppler algorithm, a two-dimensional image including at least one defocused moving target, as a training sample; generating at least one ideal Gaussian point in a position of at least one center of the at least one defocused moving target in the two-dimensional image, to generate a training label; constructing the generative adversarial network, the generative adversarial network includes a generative network and a discrimination network; inputting the training sample and the training label into the generative adversarial network to perform repeated training until an output of the generative network reaches a preset condition, to thereby obtain a trained network model; and inputting a testing sample into the trained network model, to output a moving target focused image.
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公开(公告)号：US20230162373A1
公开(公告)日：2023-05-25
申请号：US17989453
申请日：2022-11-17
Applicant: University of Electronic Science and Technology of China , Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China
Inventor： Jiang Qian , Haitao Lyu , Junzheng Jiang , Minfeng Xing
IPC: G06T7/20
CPC classification number: G06T7/20 , G06T2207/20081
Abstract: A moving target focusing method and system based on a generative adversarial network are provided. The method includes: generating, using a Range Doppler algorithm, a two-dimensional image including at least one defocused moving target, as a training sample; generating at least one ideal Gaussian point in a position of at least one center of the at least one defocused moving target in the two-dimensional image, to generate a training label; constructing the generative adversarial network, the generative adversarial network includes a generative network and a discrimination network; inputting the training sample and the training label into the generative adversarial network to perform repeated training until an output of the generative network reaches a preset condition, to thereby obtain a trained network model; and inputting a testing sample into the trained network model, to output a moving target focused image.
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公开(公告)号：US20250022107A1
公开(公告)日：2025-01-16
申请号：US18647290
申请日：2024-04-26
Inventor： Jiang Qian , Xin Ye , Haitao Lyu , Xiaobo Yang
Abstract: Provided is a fringe line detection and phase unwrapping method and system based on an FL-Net convolutional neural network. The method includes: constructing the FL-Net convolutional neural network; detecting fringe lines of an input image by using the FL-Net convolutional neural network to obtain an image with detected fringe lines; performing circulation integral on the detected fringe lines to repair the detected fringe lines to thereby obtain an image with repaired fringe lines; performing path integral on the repaired fringe lines to unwrap the repaired fringe lines to thereby obtain an image with unwrapped fringe lines; and identifying error points of the unwrapped fringe lines by using the FL-Net convolutional neural network; and processing the error points. Specifically, an HDC method is used to replace a down-sampling method to avoid the loss of resolution, and residual connection is used to prevent the network from being too deep.
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Patent Agency Ranking
公开(公告)号：US12174292B2
公开(公告)日：2024-12-24
申请号：US17989162
申请日：2022-11-17
Applicant: Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China
Inventor： Haitao Lyu , Jiang Qian , Junzheng Jiang , Minfeng Xing
Abstract: A noise suppression method and system for Inverse Synthetic Aperture Radar micro-cluster objects using a generative adversarial network (GAN) are provided. The method includes: constructing the GAN, including a generator and a discriminator; obtaining and inputting noisy simulation data into the generator to obtain a first output, comparing the first output with noiseless simulation data to obtain a first generator loss, inputting the first output and the distribution function into the discriminator for denoising discrimination to obtain a first discriminant result, and determining a second generator loss according to the first generator loss and the first discriminate result; and obtaining measured data and inputting the measured data into the generator to obtain a second output, inputting the second output to the discriminator to obtain a second discriminant result, and determining a generator loss according to the second generator and the second discriminate result.
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公开(公告)号：US12181572B1
公开(公告)日：2024-12-31
申请号：US18652208
申请日：2024-05-01
Inventor： Jiang Qian , Jiayang Wu , Haitao Lyu , Guangcai Sun
Abstract: Provided are a scattering aperture imaging method and a device, a system and a storage medium. The method mainly includes four steps of scattering point position estimation, azimuth resampling, range compensation and synthetic aperture radar imaging. A phased array radar with a fixed position is used for NLOS imaging, and the radar can control a beam to scan in space, which is equivalent to a scattering aperture moving along a relay surface. Therefore, the method can realize converting NLOS imaging into LOS synthetic aperture radar imaging, which can be suitable for the situation that a relay surface is rough and the relay surface with more complicated surface condition, thus widening the application range of radar NLOS imaging.
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公开(公告)号：US20230152444A1
公开(公告)日：2023-05-18
申请号：US17989162
申请日：2022-11-17
Applicant: Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China
Inventor： Haitao Lyu , Jiang Qian , Junzheng Jiang , Minfeng Xing
CPC classification number: G01S13/9027 , G01S7/417 , G01S13/9064
Abstract: A noise suppression method and system for Inverse Synthetic Aperture Radar micro-cluster objects using a generative adversarial network (GAN) are provided. The method includes: constructing the GAN, including a generator and a discriminator; obtaining and inputting noisy simulation data into the generator to obtain a first output, comparing the first output with noiseless simulation data to obtain a first generator loss, inputting the first output and the distribution function into the discriminator for denoising discrimination to obtain a first discriminant result, and determining a second generator loss according to the first generator loss and the first discriminate result; and obtaining measured data and inputting the measured data into the generator to obtain a second output, inputting the second output to the discriminator to obtain a second discriminant result, and determining a generator loss according to the second generator and the second discriminate result.
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7.发明公开公开(公告)号：US20240153100A1
公开(公告)日：2024-05-09
申请号：US18086582
申请日：2022-12-21
Applicant: Yangtze Delta Region Institute of University of Electronic Science and Technology of China, Huzhou
Inventor： Junzheng Jiang , Tingfang Tan , Jiang Qian
Abstract: An image foreground-background segmentation method and system based on sparse decomposition and graph Laplacian regularization are disclosed. Firstly, an image is divided into a plurality of non-overlapping image blocks; Then, a foreground-background segmentation model of the image is established according to the image blocks; An image segmentation problem is divided into several sub-problems, which are solved by iteration; Finally, after the iteration, solutions of the problem are obtained; The obtained solutions are respectively matrixed and patched to obtain a foreground image, which is a foreground image of the whole image. The image foreground-background segmentation method uses the linear combination of graph Fourier basis functions to better represent the smooth background region. In addition, the graph Laplacian regularization is used to characterize the connectivity of foreground text and graphics while keeping sharp foreground text and graphics contours. The experimental results show that this method has better foreground-background segmentation effect.
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8.发明公开公开(公告)号：US20240046602A1
公开(公告)日：2024-02-08
申请号：US18096054
申请日：2023-01-12
Applicant: Yangtze Delta Region Institute of University of Electronic Science and Technology of China, Huzhou
Inventor： Junzheng Jiang , Wanyuan Cai , Jiang Qian
IPC: G06V10/426 , G06T5/00 , G06T7/11 , G06V10/80
CPC classification number: G06V10/426 , G06T5/002 , G06T7/11 , G06V10/803 , G06T2207/10036
Abstract: Provide is a novel mixed-noise removal method for HSI with large size. First, the underlying structure of the HSI is modeled by a two-layer architecture graph. The upper layer, called a skeleton graph, is a rough graph constructed by using the modified k-nearest-neighborhood algorithm and its nodes correspond to a series of superpixels formed by HSI segmentation, which can efficiently characterize the inter-correlations between superpixels, while preserving the boundary information and reducing the computational complexity. The lower layer, called detailed graph, consists of a series of local graphs which are constructed to model the similarities between pixels. Second, based on the two-layer graph architecture, the HSI restoration problem is formulated as a series of optimization problems each of which resides on a subgraph. Third, a novel distributed algorithm is tailored for the restoration problem, by using the information interaction between the nodes of skeleton graph and subgraphs.
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