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TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images 1

16th European Conference on Computer Vision, ECCV 2020

作者： Lin, Jianxin Pang, Yingxue Xia, Yingce Chen, Zhibo Luo, Jiebo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China University of Rochester Rochester United States

ISBN: (数字)9783030585488

ISBN: (纸本)9783030585471

An unsupervised image-to-image translation (UI2I) task deals with learning a mapping between two domains without paired images. While existing UI2I methods usually require numerous unpaired images from different domains for training, there are many scenarios where training data is quite limited. In this paper, we argue that even if each domain contains a single image, UI2I can still be achieved. To this end, we propose TuiGAN, a generative model that is trained on only two unpaired images and amounts to one-shot unsupervised learning. With TuiGAN, an image is translated in a coarse-to-fine manner where the generated image is gradually refined from global structures to local details. We conduct extensive experiments to verify that our versatile method can outperform strong baselines on a wide variety of UI2I tasks. Moreover, TuiGAN is capable of achieving comparable performance with the state-of-the-art UI2I models trained with sufficient data. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Some Reflections and Simulations on the Resolution and Focusing Approach of Microwave Photonic SAR with an Across-Band Bandwidth

Some Reflections and Simulations on the Resolution and Focus...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Min Chen Xiaolan Qiu Xuejiao Wen Hui Chen Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Chinese Academy of Sciences The Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Beijing China The School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Aerospace Information Research Institute Suzhou China State key Laboratory of Millimeter Waves Southeast University Nanjing China

Microwave photonic (MWP) SAR technology can realize large bandwidth even across multiple wavebands and therefore improves the imaging resolution significantly. However, when targets are illuminated by the electromagnetic wave with such an across-band bandwidth, their scattering characteristics will change with signal frequency, which is not taken into consideration for conventional SAR imaging. In this paper, in order to investigate the resolution and optimize the imaging of MWP SAR, we first design several typical structures and conduct electromagnetic simulations on them, from which, we obtain the variation of scattering characteristics within an across-band bandwidth. Then, we use different focusing approaches for one-dimensional imaging processing and adopt different evaluation indicators to evaluate the performance of these methods comprehensively. Finally, with detailed analysis, we give suggestions on the selection of compression methods for different structures at different SNRs.

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Multi-dimensional Graph Linear Canonical Transform

arXiv

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arXiv 2024年

作者： Li, Na Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing210044 China

Many multi-dimensional (M-D) graph signals appear in the real world, such as digital images, sensor network measurements and temperature records from weather observation stations. It is a key challenge to design a transform method for processing these graph M-D signals in the linear canonical transform domain. This paper proposes the two-dimensional graph linear canonical transform based on the central discrete dilated Hermite function (2-D CDDHFs-GLCT) and the two-dimensional graph linear canonical transform based on chirp multiplication-chirp convolution-chirp multiplication decomposition (2-D CM-CC-CM-GLCT). Then, extending 2-D CDDHFs-GLCT and 2-D CM-CC-CM-GLCT to M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT. In terms of the computational complexity, additivity and reversibility, M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT are compared. Theoretical analysis shows that the computational complexity of M-D CM-CC-CM-GLCT algorithm is obviously reduced. Simulation results indicate that M-D CM-CC-CM-GLCT achieves comparable additivity to M-D CDDHFs-GLCT, while M-D CM-CC-CM-GLCT exhibits better reversibility. Finally, M-D GLCT is applied to data compression to show its application advantages. The experimental results reflect the superiority of M-D GLCT in the algorithm design and implementation of data compression. Copyright © 2024, The Authors. All rights reserved.

关键词： Chirp modulation

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Graph Chirp Signal and Graph Fractional Vertex-Frequency Energy Distribution

arXiv

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arXiv 2025年

作者： Cui, Manjun Zhang, Zhichao School of Mathematics and Statistics The Center for Applied Mathematics of Jiangsu Province The Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing University of Information Science and Technology Nanjing210044 China School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University Haikou571158 China

Graph signal processing (GSP) has emerged as a powerful framework for analyzing data on irregular domains. In recent years, many classical techniques in signal processing (SP) have been successfully extended to GSP. Among them, chirp signals play a crucial role in various SP applications. However, graph chirp signals have not been formally defined despite their importance. Here, we define graph chirp signals and establish a comprehensive theoretical framework for their analysis. We propose the graph fractional vertex-frequency energy distribution (GFED), which provides a powerful tool for processing and analyzing graph chirp signals. We introduce the general fractional graph distribution (GFGD), a generalized vertex-frequency distribution, and the reduced interference GFED, which can suppress cross-term interference and enhance signal clarity. Furthermore, we propose a novel method for detecting graph signals through GFED domain filtering, facilitating robust detection and analysis of graph chirp signals in noisy environments. Moreover, this method can be applied to real-world data for denoising more effective than some state-of-the-arts, further demonstrating its practical significance. Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

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Temporal Context Mining for Learned Video Compression

arXiv

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arXiv 2021年

作者： Sheng, Xihua Li, Jiahao Li, Bin Li, Li Liu, Dong Lu, Yan The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China Microsoft Research Asia Beijing100080 China

Applying deep learning to video compression has attracted increasing attention in recent few years. In this work, we address end-to-end learned video compression with a special focus on better learning and utilizing temporal contexts. We propose to propagate not only the last reconstructed frame but also the feature before obtaining the reconstructed frame for temporal context mining. From the propagated feature, we learn multi-scale temporal contexts and re-fill the learned temporal contexts into the modules of our compression scheme, including the contextual encoder-decoder, the frame generator, and the temporal context encoder. We discard the parallelizationunfriendly auto-regressive entropy model to pursue a more practical encoding and decoding time. Experimental results show that our proposed scheme achieves a higher compression ratio than the existing learned video codecs. Our scheme also outperforms x264 and x265 (representing industrial software for H.264 and H.265, respectively) as well as the official reference software for H.264, H.265, and H.266 (JM, HM, and VTM, respectively). Specifically, when intra period is 32 and oriented to PSNR, our scheme outperforms H.265-HM by 14.4% bit rate saving;when oriented to MS-SSIM, our scheme outperforms H.266-VTM by 21.1% bit rate saving. Copyright © 2021, The Authors. All rights reserved.

关键词： Image compression

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A FEATURE ENHANCEMENT METHOD BASED ON THE SUB-APERTURE DECOMPOSITION FOR ROTATING FRAME SHIP DETECTION IN SAR IMAGES

A FEATURE ENHANCEMENT METHOD BASED ON THE SUB-APERTURE DECOM...

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2021 IEEE International geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Lei, Songlin Qiu, Xiaolan Ding, Chibiao Lei, Shujie National Key Laboratory of Microwave Imaging Technology Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Radio Equipment Research Institute Shanghai201100 China

ISBN: (纸本)9781665403696

Deep learning algorithms are widely used in SAR target detection. At present, most detection methods based on neural networks treat SAR images as optical images for processing, and do not fully exploit the characteristics of SAR images. In this paper, a method for feature enhancement using sub-aperture decomposition is proposed. Considering the advantages of the rotate box in eliminating interference, we conducted experiments on the rotating frame detection network Rotate RetinaNet using the Complex SAR images Rotation Ship Detection Dataset (CSRSDD). Compared with the base method, AP improves by 2.4% without bells and whistles. The results confirm the effectiveness of the proposed method. © 2021 IEEE

关键词： Neural networks Optical computing Object detection Optical fiber networks Optical detectors Optical imaging Radar polarimetry

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A Comparative Study of Single-polarized and Polarimetric SAR Images Applied on Unsupervised Classification

A Comparative Study of Single-polarized and Polarimetric SAR...

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IEEE International Conference on Radar

作者： Junrong Qu Xiaolan Qiu Key Laboratory of Technology in Geo-spatial Information and Application System Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665468893

SAR image understanding is a significant but also challenging issue in practice. In order to detect the difference between single-polarized and polarimetric SAR (PolSAR) data and explore more information from the pre one, we use GF3 data for experiments and adopt the Deep Clustering with Convolutional Autoencoders (DCEC) algorithm for single-polarized SAR image unsupervised classification. Using both the Kappa coefficient and mutual information derived from the confusion matrix, the results of single-polarized SAR data and PolSAR data are compared. It shows that the classification results of single-polarized SAR data match with the polarimetric ones to some extent, and their results are comparable in some specific applications. This paper presents the potential of information mining from single-polarized SAR images, as well as gives some references for the selection and trade-off between single- and full-polarized SAR data.

关键词： Image resolution Correlation Clustering algorithms Scattering Radar imaging Radar polarimetry Classification algorithms

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Ocean Oil Spill Classification with Polarimetric SAR Based on VGG16 Multi-Feature Extraction

Ocean Oil Spill Classification with Polarimetric SAR Based o...

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2021 SAR in Big Data Era, BIGSARDATA 2021

作者： Ren, Huimin Li, Fangfang Han, Bing Hong, Wen Dong, Yingbo Wu, Bitong QiLu Research Institute Aerospace Information Research Institute Chinese Academy of Sciences Jinan China Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Technology in Geospatial Information Processing and Application System Beijing China

ISBN: (纸本)9781665401234

Fully polarimetric synthetic aperture radar (PolSAR) technology performs well in oil spill detection because of its rich target scattering information. Aiming at the problem that oil spill detection scheme based on polarization feature and deep learning has large sample requirements and high information redundancy, we propose a classification scheme combining with RBFSVM classifier and VGG16 based on transfer network. The ability of our method to distinguish different oil films is analyzed through a radarsat-2 POLSAR image of the North Sea oil spill experiment. © 2021 IEEE.

关键词： Oil spills

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An adaptive Enhanced Imaging Method for Multi-aspect SAR

An adaptive Enhanced Imaging Method for Multi-aspect SAR

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2021 CIE International Conference on Radar, Radar 2021

作者： Teng, Fei Lin, Yun Yue, Xiaoyang Feng, Shanshan Hong, Wen Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Information Engineering North China University of Technology Beijing China

ISBN: (纸本)9781665498142

Compared with traditional SAR working modes, multi-aspect SAR can provide images with higher resolution and signal-to-noise ratio (SNR) due to its larger synthetic aperture. However, the SNR does not increase with the increase of the aperture length. This is because the scattering is no longer isotropic as traditional SAR when the viewing angle is large. In this paper, an adaptive enhanced imaging method for multi-aspect SAR is proposed. The resolution and the SNR are maximized by scattering analysis performed simultaneously with imaging. In the process of image generation, the scattering characteristic is analyzed and all targets are divided into two categories: isotropic and anisotropic. Then different image formation strategies are used for isotropic and anisotropic target. A C-band circular SAR data is used to validate our method. © 2021 IEEE.

关键词： adaptive anisotropic scattering imaging Multi-aspect SAR

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Efficient Sparse MIMO SAR Imaging with Fast Iterative Method Based on Back Projection and Approximated Observation

Efficient Sparse MIMO SAR Imaging with Fast Iterative Method...

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Electronics and Electrical Engineering technology (EEET), International Conference on

作者： Pengyu Jiang Zhe Zhang Bingchen Zhang Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Aerospace Information Research Institute Suzhou China

Compressed sensing technique is widely used in the field of multiple input multiple output (MIMO) radar imaging to suppress sidelobes and noise, bringing better imaging performance. However, many constraints are introduced to the sparse reconstruction model, current sparse microwave imaging model is based on several assumptions, such as far field assumption, which might not hold in some circumstances. In this paper, a novel two-dimensional sparse reconstruction method based on approximated observation is proposed. First, we obtain the measurement matrix by the inverse back-projection operator, and construct a two-dimensional sparse reconstruction model. Then, we adopt the minimax concave constraint as the regularization term and use the iterative soft threshold algorithm (ISTA) for sparse reconstruction. The method retains the advantages of the BP algorithm and the sparse reconstruction algorithm at the same time, and the distance between the target and the antenna array is not constrained by image reconstruction performance of the sparse reconstruction algorithm. Simulations and experiments demonstrate the effectiveness of the proposed method.

关键词： MIMO radar Simulation Imaging Radar imaging Reconstruction algorithms Sparse matrices Velocity measurement

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