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16th European Conference on Computer Vision, ECCV 2020

作者： Liu, Jianzhao Lin, Jianxin Li, Xin Zhou, Wei Liu, Sen Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9783030585228

Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task. To alleviate this problem, we raise the novel lifelong image restoration problem for blended distortions. We first design a base fork-join model in which multiple pre-trained expert models specializing in individual distortion removal task work cooperatively and adaptively to handle blended distortions. When the input is degraded by a new distortion, inspired by adult neurogenesis in human memory system, we develop a neural growing strategy where the previously trained model can incorporate a new expert branch and continually accumulate new knowledge without interfering with learned knowledge. Experimental results show that the proposed approach can not only achieve state-of-the-art performance on blended distortions removal tasks in both PSNR/SSIM metrics, but also maintain old expertise while learning new restoration tasks. © 2020, Springer Nature Switzerland AG.

关键词： Image reconstruction

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Algorithms and statistical analysis for linear structured weighted total least squares problem

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geodesy and geodynamics 2024年第2期15卷 177-188页

作者： Jian Xie Tianwei Qiu Cui Zhou Dongfang Lin Sichun Long School of Earth Science and Spatial Information Engineering Hunan University of Science and TechnologyXiangtan 411201China College of Science Central South University of Forestry and TechnologyChangsha 410018China National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and TechnologyXiangtan 411201China

Weighted total least squares(WTLS)have been regarded as the standard tool for the errors-in-variables(EIV)model in which all the elements in the observation vector and the coefficient matrix are contaminated with random ***,in many geodetic applications,some elements are error-free and some random observations appear repeatedly in different positions in the augmented coefficient *** is called the linear structured EIV(LSEIV)*** kinds of methods are proposed for the LSEIV model from functional and stochastic *** the one hand,the functional part of the LSEIV model is modified into the errors-in-observations(EIO)*** the other hand,the stochastic model is modified by applying the Moore-Penrose inverse of the cofactor *** algorithms are derived through the Lagrange multipliers method and linear *** estimation principles and iterative formula of the parameters are proven to be *** first-order approximate variance-covariance matrix(VCM)of the parameters is also derived.A numerical example is given to compare the performances of our proposed three algorithms with the STLS ***,the least squares(LS),total least squares(TLS)and linear structured weighted total least squares(LSWTLS)solutions are compared and the accuracy evaluation formula is proven to be feasible and ***,the LSWTLS is applied to the field of deformation analysis,which yields a better result than the traditional LS and TLS estimations.

关键词： Linear structured weighted total least squares Errors-in-variables Errors-in-observations Functional modelmodification Stochastic model modification Accuracyevaluation

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iWavePro: An improved framework for iWave++ 56

iWavePro: An improved framework for iWave++

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56th IEEE International Symposium on Circuits and Systems, ISCAS 2023

作者： Xue, Dongmei Dong, Cunhui Ye, Fan Chen, Hang Kang, Bowei Li, Li Liu, Dong University of Science and Technology of China Cas Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Hefei230027 China

ISBN: (纸本)9781665451093

End-to-end image coding methods based on wavelet-like transform have made great progress in recent years. The most advanced one is iWave++, which adopts multi-level lifting schemes based on convolutional neural networks. However, iWave++ still has many unresolved problems. First, the independent entropy coding of each component makes it impossible to use the correlation between components better. Secondly, additive wavelet transform limits the nonlinear ability of learnable wavelet transform. Moreover, the offline training strategy makes the iWave++ unable to adjust according to the content. In this paper, we propose an improved framework for iWave++ called iWave-Pro. iWavePro is designed with several techniques to overcome the problems mentioned above. These techniques are the joint multi-component Gaussian mixture entropy coding, the affine wavelet-like transform, and the online training. Experimental results show that our method can save 10.73% bit rate compared with iWave++ at the same quality. © 2023 IEEE.

关键词： Convolutional neural networks

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Stabilization of time-delay Markovian jump systems via probability rate synthesis and state feedback 19

Stabilization of time-delay Markovian jump systems via proba...

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19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014

作者： Ma, Shan Xiong, Junlin CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9783902823625

This paper considers the stabilization problem for Markovian jump systems with time delays. Both the probability rate matrix and the state feedback control law are to be designed. A sufficient condition is established for such designs such that the resulting closed-loop Markovian jump system is stochastically stable. This condition is given in terms of a system of linear matrix inequalities with rank constraints, and can be solved using some existing algorithms. When the system has polytopic uncertainties, the robust stabilization problem is studied as well. Finally, a numerical example is given to show the validity of the proposed method. © IFAC.

关键词： Markov processes

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FAN: Frequency Aggregation Network for Real Image Super-Resolution 16th

FAN: Frequency Aggregation Network for Real Image Super-Reso...

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Workshops held at the 16th European Conference on Computer Vision, ECCV 2020

作者： Pang, Yingxue Li, Xin Jin, Xin Wu, Yaojun Liu, Jianzhao Liu, Sen Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9783030670696

Single image super-resolution (SISR) aims to recover the high-resolution (HR) image from its low-resolution (LR) input image. With the development of deep learning, SISR has achieved great progress. However, It is still a challenge to restore the real-world LR image with complicated authentic degradations. Therefore, we propose FAN, a frequency aggregation network, to address the real-world image super-resolu-tion problem. Specifically, we extract different frequencies of the LR image and pass them to a channel attention-grouped residual dense network (CA-GRDB) individually to output corresponding feature maps. And then aggregating these residual dense feature maps adaptively to recover the HR image with enhanced details and textures. We conduct extensive experiments quantitatively and qualitatively to verify that our FAN performs well on the real image super-resolution task of AIM 2020 challenge. According to the released final results, our team SR-IM achieves the fourth place on the X4 track with PSNR of 31.1735 and SSIM of 0.8728. © 2020, Springer Nature Switzerland AG.

关键词： Textures

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A decomposed dual-cross generative adversarial network for image rain removal 29

A decomposed dual-cross generative adversarial network for i...

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29th British Machine Vision Conference, BMVC 2018

作者： Jin, Xin Chen, Zhibo Lin, Jianxin Chen, Jiale Zhou, Wei Shan, Chaowei CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Rain removal is important for many computer vision applications, such as surveillance, autonomous car, etc. Traditionally, rain removal is regarded as a signal removal problem which usually causes over-smoothing by removing texture details in non-rain background regions. This paper considers the issue of rain removal from a completely different perspective, to treat rain removal as a signal decomposition problem. Specifically, we decompose the rain image into two components, namely non-rain background image and rain streaks image. Then, we introduce an adversarial training mechanism to synthesize non-rain background image and rain streaks image in a Dual-Cross manner, which makes the two adversarial branches interact with each other, archiving a win-win result ultimately. The proposed Decomposed Dual-Cross Generative Adversarial Network (DDC-GAN) shows significantly performance improvement compared with state-of-the-art methods on both synthetic and real-world images in terms of qualitative and quantitative measures (over 3dB gains in PSNR). © 2018. The copyright of this document resides with its authors.

关键词： Rain

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Unambiguous parameter estimation of radial velocity approach for airborne SAR-GMTI

Unambiguous parameter estimation of radial velocity approach...

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2011 3rd International Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2011

作者： Xu, Ruipeng Zhang, Dandan Huang, Lijia Hu, Donghui Ding, Chibiao Key Laboratory of Geo-spatial Information Processing and Application System Technology Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9788993246179

The ambiguous Doppler centroid causes incorrect estimation result of the radial velocity. For moving targets with fast radial velocity, an unambiguous estimation approach of the radial velocity is introduced for the airborne SAR system in this paper. First, two channels carried different wavelength can be constructed by range multilook processing (RMLP). Second, the instantaneous frequency curve of the moving target is obtained by polynomial fitting for each channel, where the linear term denotes the baseband Doppler centroid. Considering the fact that the difference of the estimated baseband Doppler centroid in two channels is equal to the difference of the absolute Doppler centroid, the unambiguous radial velocity can be estimated using it. Third, the range walk and the Doppler shift which are produced by the fast radial velocity can be eliminated by compensating a phase in the range-frequency and azimuth-time domain. As a result, the moving target will be reimaged and relocated after azimuth compression. The proposed approach will be applied to both single- and multi-channel SAR system. The simulation results validate the effectiveness of the proposed method. © 2011 KIEES.

关键词： Parameter estimation

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The Research on the Space-Time Variation of Phase Imbalance for GF-3 Azimuth Multichannel Mode 6

The Research on the Space-Time Variation of Phase Imbalance ...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Shang, Mingyang Qiu, Xiaolan Han, Bing Zhong, Lihua DIng, Chibiao Yang, Junying Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728129129

Azimuth multichannel (AMC) synthetic aperture radar (SAR) is an advanced technique which can prevent the minimum antenna area constraint and provide high-resolution and wide-swath (HRWS) SAR images. Channel imbalance calibration is an important topic in AMC SAR signal processing, since it has a great impact on the image quality. The phase imbalance of AMC SAR is not only related to the radar system, but also to the satellite attitude. This paper first models the relationship between phase imbalance and satellite attitude, and analyzes the influence of satellite attitude on the phase imbalance. In the presence of the satellite attitude deviation, the phase imbalance is time-variation in azimuth, and spatial-variation in range. Then, the real data acquired by Gaofen-3 (GF-3) in the ultra-fine strip-map (UFS) mode is used to verify the relationship model both in theoretical calculations and estimation. The result shows that the model proposed in this paper can describe the space-time variation of phase imbalance very accurately. Finally, some discussions and conclusions are drawn and the future research perspectives are proposed. © 2019 IEEE.

关键词： Synthetic aperture radar

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An Approach for Spaceborne InSAR DEM Inversion Integrated with Stereo-SAR Method 6

An Approach for Spaceborne InSAR DEM Inversion Integrated wi...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Fangfang Zhang, Yueting Qiu, Xiaolan Key Laboratory of Geo-spatial Information Processing and Application System Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728129129

Spaceborne Interferometric Synthetic Aperture Radar (InSAR) has the capability of high precise topographic mapping for large area. However, on the one hand, digital elevation models (DEM) inversion needs at least one ground control points (GCP) to obtain absolute interferometric phase as the unwrapped phase is just a relative value. On the other hand, the baseline value used for DEM inversion also needs to be calibrated using GCPs due to the measure error. In practice, it is difficult to manually deploy GCPs especially in the area of steep topography. At present, with the improvement of orbital measure accuracy and system time accuracy for spaceborne SAR satellite, high precise absolute 3D location information of some stable point scatterers can be obtained by stereo SAR using data from different viewing angles. Therefore, this paper combines stereo SAR with InSAR. These point scatters are used as GCPs to calibrate baseline and estimate absolute phase. As a result, high accurate DEM generation without GCPs can be achieved. The experiment using TerraSAR-X data verifies the effectiveness of this method. © 2019 IEEE.

关键词： Synthetic aperture radar

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Improved S2ANet based on attention mechanism for small target detection in remote sensing images

Improved S2ANet based on attention mechanism for small targe...

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2021 CIE International Conference on Radar, Radar 2021

作者： Lu, Dongdong Tie, Wenjie Lei, Songlin Qiu, Xiaolan Suzhou Aerospace Information Research Institute Laboratory of Spatial Information Intelligent Processing System Suzhou China Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Beijing China

ISBN: (纸本)9781665498142

Deep learning has achieved remarkable results in the field of target detection and recognition. For small targets in images, image pyramid can be used to fuse multi-scale features to improve detection performance. However, when test on remote sensing images, it is found that some important small goals will still be missed. Aiming at the problems of small target detection in remote sensing images, this paper proposes two S2ANet improved network variants based on the attention mechanism for synthetic aperture radar(SAR) payload and optical payload: for SAR images, CBAM(Convolutional Block Attention Module) attention is added to the backbone network to increase the attention of the feature extraction network for small targets in the channel and space dimensions;For optical images, firstly increase the CBAM channel attention and spatial attention in the backbone, secondly, the SE(Squeeze And Excitation) channel attention is added to the input features before the channel dimension fusion of the pyramid structure, then the attention-enhanced targets are fully integrated through up-sampling and horizontal splicing operations to improve the missed alarm rate and recall rate of small remote sensing targets. Finally, a comparative verification was carried out on the constructed airborne KU-band SAR data set and the optical remote sensing aircraft data set composed of some DOTA1.5 and FAIR1M data sets. The experimental results proved that the designed two variant networks can be used for small remote sensing. There have been certain improvements in the missed alarm rate, accuracy rate, and recall rate of the target. © 2021 IEEE.

关键词： Radar remote sensing Radar detection Object detection Radar imaging Optical imaging Feature extraction Adaptive optics

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