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Prior-enlightened and motion-robust video deblurring

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arXiv 2020年

作者： Zhou, Ya Xu, Jianfeng Tasaka, Kazuyuki Chena, Zhibo Li, Weiping CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230026 China KDDI Research Inc. Fujimino-shi Saitama356-8502 Japan

Various blur distortions in video will cause negative impact on both human viewing and video-based applications, which makes motion-robust deblurring methods urgently needed. Most existing works have strong dataset dependency and limited generalization ability in handling challenging scenarios, like blur in low contrast or severe motion areas, and non-uniform blur. Therefore, we propose a PRiOr-enlightened and MOTION-robust video deblurring model (PROMOTION) suitable for challenging blurs. On the one hand, we use 3D group convolution to efficiently encode heterogeneous prior information, explicitly enhancing the scenes’ perception while mitigating the output’s artifacts. On the other hand, we design the priors representing blur distribution, to better handle non-uniform blur in spatio-temporal domain. Besides the classical camera shake caused global blurry, we also prove the generalization for the downstream task suffering from local blur. Extensive experiments demonstrate we can achieve the state-of-the-art performance on well-known REDS and GoPro datasets, and bring machine task gain. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion estimation

Stereo Matching of VHR Remote Sensing Images via Bidirectional Pyramid Network

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Stereo Matching of VHR Remote Sensing Images via Bidirection...

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Rongshu Tao Yuming Xiang Hongjian You Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Stereo matching, an essential step in 3D reconstruction, still faces unignorable problems due to the very high resolution and complex structures of remote sensing images. Especially in occluded areas of high buildings and untextured areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we propose a novel method based on the pyramid stereo matching network to solve the aforementioned problems. Inspired by the classical optical flow estimation framework, we adopt the forward-backward consistency assumption to improve the accuracy. Moreover, we improve the construction of cost volume since the traditional deep-learning networks only work well for positive disparities and the disparity ranges in remote sensing images vary a lot. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two baselines in terms of average endpoint error (EPE) and the fraction of erroneous pixels(D1), and the improvements in occluded areas are significant.

关键词： Estimation Remote sensing Optical sensors Optical imaging Three-dimensional displays Buildings Satellites

Beyond triplet loss: Meta prototypical N-tuple loss for person re-identification

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arXiv 2020年

作者： Zhang, Zhizheng Lan, Cuiling Zeng, Wenjun Chen, Zhibo Chang, Shih-Fu The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Anhui Hefei China Microsoft Research Asia Beijing China Columbia University New YorkNY United States

Person Re-identification (ReID) aims at matching a person of interest across images. In convolutional neural network (CNN) based approaches, loss design plays a vital role in pulling closer features of the same identity and pushing far apart features of different identities. In recent years, triplet loss achieves superior performance and is predominant in ReID. However, triplet loss considers only three instances of two classes in per-query optimization (with an anchor sample as query) and it is actually equivalent to a two-class classification. There is a lack of loss design which enables the joint optimization of multiple instances (of multiple classes) within per-query optimization for person ReID. In this paper, we introduce a multi-class classification loss, i.e., N-tuple loss, to jointly consider multiple (N) instances for per-query optimization. This in fact aligns better with the ReID test/inference process, which conducts the ranking/comparisons among multiple instances. Furthermore, for more efficient multi-class classification, we propose a new meta prototypical N-tuple loss. With the multi-class classification incorporated, our model achieves the state-of-the-art performance on the benchmark person ReID datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

Research on Integrated Deployment Method of Digital Earth Thematic application Model 1

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Research on Integrated Deployment Method of Digital Earth Th...

1st China Digital Earth Conference, CDEC 2019

作者： Danpu, Zhao Xianqing, Tai Zhijie, Ma Key Laboratory of Technology in Geospatial Information Processing and Application System Institute of Elec-tronics Chinese Academy of Sciences Beijing100190 China Research Center of Intelligent City and Intelligent Transportation CAS R and D Center for Internet of Things Wuxi214135 China

Nowadays, the digital earth not only relates to the technologies of surveying and mapping geography, but also includes the analysis and cross-application of various scientific data related to geographic information. It requires a computing environment and management for the corresponding thematic application models which supports the online analysis on the digital earth. So we proposed an integrated deployment method for digital earth thematic application models based on container cloud, which provides a flexible and scalable information integration and analysis architecture for digital earth. Firstly, a Docker image building module is designed to facilitate the user to quickly build the application model into a image. Then the Docker technology is used to implement the containerization of the application model to provide a stable running environment for the application model. Finally, the Kubernetes is used to dynamically manage the cluster resources to realize rapid expansion of computing resources. In addition, the HDFS is deployed, and the image files and various scientific data respectively store with Hbase and Apache file systems. It can ensure the read and write speed of the application model and the related data and log files. It also used zabbix framework to manage the monitoring index of the container cloud cluster to ensure the execution effect of the application model. Through the tests of typical application model, it is verified that the method can integrated deploy and apply the cross-domain, hetero-architecture, multi-version and multi-class application models on the digital earth. © Published under licence by IOP Publishing Ltd.

关键词： Containers

Phase Transition Analysis for Compressive Sensing Based DoA Estimation in Automotive Radar

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Phase Transition Analysis for Compressive Sensing Based DoA ...

International Radar Symposium (IRS)

作者： Yan Zhang Silin Gao Mingqian Liu Bingchen Zhang Yirong Wu Chinese Academy of Sciences Aerospace Information Research Institute Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information and Processing Application System Beijing China

ISBN: (数字)9788394942151

ISBN: (纸本)9781728157863

In automotive radar applications, the compressive sensing (CS) based DoA estimation is used in array signal processing in recent years. Sparse reconstruction has the potential to estimate the direction of arrival (DoA) with super resolution. However, failed results may be acquired via sparse reconstruction in inappropriate conditions, namely the critical condition determining success or failure must be taken into consideration. In this paper, the sparsity of the scenario and the signal-to-noise ratio (SNR) are analyzed as the main factors via phase transition diagrams. Other factors affecting the success or failure are also investigated, such as the array configuration and the sparse recovery algorithm. Simulated and experimental results demonstrate the critical conditions, in which the DoA estimation is successful or failed.

关键词： Direction-of-arrival estimation Estimation Radar applications Compressed sensing Signal resolution Signal to noise ratio Automotive engineering

On the Use of Axial Ratio to Analysis Transmit Distortions in Hybrid Polarimetry and Their Influence to Canonical Scattering Mechanisms 6

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On the Use of Axial Ratio to Analysis Transmit Distortions i...

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

作者： Chen, Shiqiang Hong, Wen Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728129129

Transmit distortions in the hybrid polarimetric (HP) SAR cannot be compensated simply with external calibration methods. Therefore, it is necessary to analysis their influence on HP data. In this study, we have proposed an analysis method based on the Axial Ratio (AR) of the real transmitted polarized wave. Firstly, impact of different distortion sources is quantified with the AR parameter. Next, a case study is illustrated using real GF-3 measured data. At last, the influence of transmit distortions to canonical scattering mechanisms is analyzed. © 2019 IEEE.

关键词： Polarimeters

Region Normalization for Image Inpainting

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arXiv 2019年

作者： Yu, Tao Guo, Zongyu Jin, Xin Wu, Shilin Chen, Zhibo Li, Weiping Zhang, Zhizheng Liu, Sen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China China

Feature Normalization (FN) is an important technique to help neural network training, which typically normalizes features across spatial dimensions. Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e.g. mean and variance shifts. In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. RN divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization. We develop two kinds of RN for our image inpainting network: (1) Basic RN (RN-B), which normalizes pixels from the corrupted and uncorrupted regions separately based on the original inpainting mask to solve the mean and variance shift problem;(2) Learnable RN (RN-L), which automatically detects potentially corrupted and uncorrupted regions for separate normalization, and performs global affine transformation to enhance their fusion. We apply RN-B in the early layers and RN-L in the latter layers of the network respectively. Experiments show that our method outperforms current state-of-the-art methods quantitatively and qualitatively. We further generalize RN to other inpainting networks and achieve consistent performance improvements. Our code is available at https://***/geekyutao/RN. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

SAR image classification based on spiking neural network through spike-time dependent plasticity and gradient descent

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arXiv 2021年

作者： Chen, Jiankun Qiu, Xiaolan Ding, Chibiao Wu, Yirong National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Aerospace Information Research Institute Beijing10090 China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing10090 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Suzhou Research Institute Aerospace Information Research Institute Chinese Academy of Sciences Suzhou215123 China The University of Chinese Academy of Sciences Beijing10049 China

At present, the Synthetic Aperture Radar (SAR) image classification method based on convolution neural network (CNN) has faced some problems such as poor noise resistance and generalization ability. Spiking neural network (SNN) is one of the core components of brain-like intelligence and has good application prospects. This article constructs a complete SAR image classifier based on unsupervised and supervised learning of SNN by using spike sequences with complex spatio-temporal information. We firstly expound the spiking neuron model, the receptive field of SNN, and the construction of spike sequence. Then we put forward an unsupervised learning algorithm based on STDP and a supervised learning algorithm based on gradient descent. The average classification accuracy of single layer and bilayer unsupervised learning SNN in three categories images on MSTAR dataset is 80.8% and 85.1%, respectively. Furthermore, the convergent output spike sequences of unsupervised learning can be used as teaching signals. Based on the TensorFlow framework, a single layer supervised learning SNN is built from the bottom, and the classification accuracy reaches 90.05%. By comparing noise resistance and model parameters between SNNs and CNNs, the effectiveness and outstanding advantages of SNN are verified. Code to reproduce our experiments is available at https://***/Jiankun-chen/Supervised-SNN-with-GD. © 2021, CC BY.

关键词： Gradient methods

An Energy Efficient Carry-Free Inner Product Unit

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An Energy Efficient Carry-Free Inner Product Unit

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Yan, Wen Ercegovac, Milos D. Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China University of California Computer Science Department Los AngelesCA United States

ISBN: (纸本)9781728123455

An energy efficient truncated inner product unit is proposed in this paper. The proposed unit is pipelined and processes the m pairs of n-bit operands in serial, so that only one unit is required and it can be reused in each recurrence. Truncated inner product is produced so that no accumulation is needed for the lower part. The array reduction unit is bit-level pipelined to improve throughput and reduce the initial delay. A two-level [4:2] adder is proposed to accumulate redundant vectors with non-uniform input arrival. The proposed inner product is compared with other truncated inner product units using fully merged arithmetic, partially merged arithmetic and the counter based inner product for different precision and vector length. Our proposed inner product unit has significant reduction in area, power and energy. © 2019 IEEE.

关键词： Pipelines