Publications

2022

1. 

   Learning to Super-Resolve Blurry Images with Events

   Yu Lei ✉️, Wang Bishan, Zhang Xiang, Zhang Haijian, Yang Wen, Liu Jianzhuang, and Xia Guisong

   IEEE Transactions on Pattern Recognition and Machine Intelligence (TPAMI), 2022

   🔥 Top Journal

   Bib Code

   @article{yu2022b,
     author = {Yu, Lei and Wang, Bishan and Zhang, Xiang and Zhang, Haijian and Yang, Wen and Liu, Jianzhuang and Xia, Guisong},
     journal = {IEEE Transactions on Pattern Recognition and Machine Intelligence (TPAMI)},
     number = {0},
     title = {Learning to Super-Resolve Blurry Images with Events},
     volume = {0},
     year = {2022}
   }

2. 

   Spiking Sparse Recovery with Non-convex Penalties

   Zhang Xiang, Yu Lei ✉️, Zheng Gang, and Eldar Yonina

   IEEE Transactions on Signal Processing, 2022

   Bib

   @article{yu2022a,
     title = {Spiking Sparse Recovery with Non-convex Penalties},
     author = {Zhang, Xiang and Yu, Lei and Zheng, Gang and Eldar, Yonina},
     year = {2022},
     journal = {IEEE Transactions on Signal Processing}
   }

3. 

   Synthetic aperture imaging with events and frames

   Liao Wei, Zhang Xiang, Yu Lei ✉️, Lin Shijie, Yang Wen, and Qiao Ning

   In CVPR, 2022

   🔥 A new SAI method by fusing events and frames.

   Bib Code

   @inproceedings{Liao2022,
     title = {Synthetic aperture imaging with events and frames},
     author = {Liao, Wei and Zhang, Xiang and Yu, Lei and Lin, Shijie and Yang, Wen and Qiao, Ning},
     booktitle = {CVPR},
     year = {2022}
   }

4. 

   Autofocus for event cameras

   Lin Shijie, Zhang Y, Yu Lei, Zhou B., Luo X., and Pan Jian

   In CVPR, 2022

   🔥 Oral paper

   arXiv Bib Code

   @inproceedings{Lin2022,
     title = {Autofocus for event cameras},
     author = {Lin, Shijie and Zhang, Y and Yu, Lei and Zhou, B. and Luo, X. and Pan, Jian},
     booktitle = {CVPR},
     year = {2022}
   }

5. 

   Unifying motion deblurring and frame interpolation with events

   Zhang Xiang, and Yu Lei ✉️

   In CVPR, 2022

   🔥 Self-supervised EVDI

   arXiv Bib Code

   @inproceedings{Zhang2022,
     title = {Unifying motion deblurring and frame interpolation with events},
     author = {Zhang, Xiang and Yu, Lei},
     booktitle = {CVPR},
     year = {2022}
   }

6. 

   Learning to Extract Building Footprints from Off-Nadir Aerial Images

   Wang Jinwang, Meng L., Li W., Yang Wen, Yu Lei, and Xia Gui-Song

   IEEE Transactions on Pattern Recognition and Machine Intelligence (TPAMI), 2022

   🔥 Top Journal

   Bib HTML

   @article{wang2022,
     title = {Learning to Extract Building Footprints from Off-Nadir Aerial Images},
     journal = {IEEE Transactions on Pattern Recognition and Machine Intelligence (TPAMI)},
     author = {Wang, Jinwang and Meng, L. and Li, W. and Yang, Wen and Yu, Lei and Xia, Gui-Song},
     year = {2022}
   }

7. 

   RFLA: Gaussian Receptive Field Based Label Assignment for Tiny Object Detection

   Xu Chang, Wang Jinwang, Yang Wen, Yu Huai, Yu Lei, and Xia Gui-Song

   In European Conference on Computer Vision, 2022

   Bib Code

   @inproceedings{xu2022rfla,
     title = {RFLA: Gaussian Receptive Field Based Label Assignment for Tiny Object Detection},
     author = {Xu, Chang and Wang, Jinwang and Yang, Wen and Yu, Huai and Yu, Lei and Xia, Gui-Song},
     booktitle = {European Conference on Computer Vision},
     pages = {526--543},
     year = {2022},
     organization = {Springer}
   }

8. 

   Learning to See Through with Events

   Yu Lei ✉️, Zhang Xiang, Liao Wei, Yang Wen, and Xia Gui-Song

   IEEE Transactions on Pattern Recognition and Machine Intelligence (TPAMI), 2022

   🔥 Top Journal!

   Bib HTML PDF Code Dataset

   @article{yu2022,
     title = {Learning to See Through with Events},
     author = {Yu, Lei and Zhang, Xiang and Liao, Wei and Yang, Wen and Xia, Gui-Song},
     year = {2022},
     journal = {IEEE Transactions on Pattern Recognition and Machine Intelligence (TPAMI)},
     dataset = {https://drive.google.com/file/d/19SnoRfVgvKlUspAyzbBhtXszXkh5NsbS/view?usp=sharing}
   }

2021

1. 

   Single Image Deraining With Continuous Rain Density Estimation

   Yu Lei ✉️, Wang Bishan, He Jingwei, Xia Guisong, and Yang Wen

   IEEE Transaction on Multimedia, 2021

   🔥 A new deraining method considering rain densities.

   Bib Code

   @article{key,
     author = {Yu, Lei and Wang, Bishan and He, Jingwei and Xia, Guisong and Yang, Wen},
     journal = {IEEE Transaction on Multimedia},
     number = {0},
     title = {Single Image Deraining With Continuous Rain Density Estimation},
     volume = {0},
     year = {2021}
   }

2. 

   Event-based Synthetic Aperture Imaging with a Hybrid Network

   Zhang Xiang, Liao Wei, Yu Lei ✉️, Yang Wen, and Xia Gui-Song

   In CVPR, 2021

   🔥 Oral, Best Paper Candidate

   arXiv Bib Code Slides

   @inproceedings{zhang2021,
     title = {Event-based {Synthetic} {Aperture} {Imaging} with a {Hybrid} {Network}},
     booktitle = {CVPR},
     author = {Zhang, Xiang and Liao, Wei and Yu, Lei and Yang, Wen and Xia, Gui-Song},
     year = {2021},
     pages = {14235--14244}
   }

3. 

   Motion Deblurring with Real Events

   Xu Fang, Yu Lei ✉️, Wang Bishan, Yang Wen, Xia Guisong, Jia Xu, Qiao Zhendong, and Liu Jianzhuang

   In ICCV, 2021

   🔥 A new framework to learn deblurring with real-world events

   Bib Code

   @inproceedings{xu2021,
     author = {Xu, Fang and Yu, Lei and Wang, Bishan and Yang, Wen and Xia, Guisong and Jia, Xu and Qiao, Zhendong and Liu, Jianzhuang},
     title = {Motion Deblurring with Real Events},
     booktitle = {ICCV},
     year = {2021}
   }

4. 

   Image super-resolution by learning weighted convolutional sparse coding

   He Jingwei, Yu Lei ✉️, Liu Zhou, and Yang Wen

   Signal, Image and Video Processing, 2021

   Bib

   @article{he2021,
     title = {Image super-resolution by learning weighted convolutional sparse coding},
     journal = {Signal, Image and Video Processing},
     author = {He, Jingwei and Yu, Lei and Liu, Zhou and Yang, Wen},
     year = {2021},
     pages = {1--9}
   }

5. 

   ULSD: Unified line segment detection across pinhole, fisheye, and spherical cameras

   Li Hao, Yu Huai, Wang Jinwang, Yang Wen, Yu Lei ✉️, and Scherer Sebastian

   ISPRS Journal of Photogrammetry and Remote Sensing, 2021

   🔥 Best Paper of 2021 in ISPRS

   Bib HTML Code

   @article{li2021,
     title = {{ULSD}: {Unified} line segment detection across pinhole, fisheye, and spherical cameras},
     volume = {178},
     journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
     author = {Li, Hao and Yu, Huai and Wang, Jinwang and Yang, Wen and Yu, Lei and Scherer, Sebastian},
     year = {2021},
     pages = {187--202}
   }

2020

1. 

   Distributed compressive sensing via LSTM-Aided sparse Bayesian learning

   Zhang Haijian, Zhang Wusheng, Yu Lei ✉️, and Bi Guoan

   Signal Processing, 2020

   Bib

   @article{zhang2020f,
     title = {Distributed compressive sensing via {LSTM}-{Aided} sparse {Bayesian} learning},
     volume = {176},
     journal = {Signal Processing},
     author = {Zhang, Haijian and Zhang, Wusheng and Yu, Lei and Bi, Guoan},
     year = {2020},
     pages = {107656}
   }

2. 

   Robust motion compensation for event cameras with smooth constraint

   Xu Jie, Jiang Meng, Yu Lei ✉️, Yang Wen, and Wang Wenwei

   IEEE Transactions on Computational Imaging, 2020

   Bib

   @article{xu2020a,
     title = {Robust motion compensation for event cameras with smooth constraint},
     volume = {6},
     journal = {IEEE Transactions on Computational Imaging},
     author = {Xu, Jie and Jiang, Meng and Yu, Lei and Yang, Wen and Wang, Wenwei},
     year = {2020},
     pages = {604--614}
   }

3. 

   Matching Neuromorphic Events and Color Images via Adversarial Learning

   Xu Fang, Lin Shijie, Yang Wen, Yu Lei, Dai Dengxin, and Xia Gui-song

   arXiv preprint arXiv:2003.00636, 2020

   Bib

   @article{xu2020matching,
     title = {Matching Neuromorphic Events and Color Images via Adversarial Learning},
     author = {Xu, Fang and Lin, Shijie and Yang, Wen and Yu, Lei and Dai, Dengxin and Xia, Gui-song},
     journal = {arXiv preprint arXiv:2003.00636},
     year = {2020}
   }

4. 

   Robust Intensity Image Reconstruciton Based On Event Cameras

   Jiang Meng, Liu Zhou, Wang Bishan, Yu Lei, and Yang Wen

   In 2020 IEEE International Conference on Image Processing (ICIP), 2020

   Bib

   @inproceedings{jiang2020robust,
     title = {Robust Intensity Image Reconstruciton Based On Event Cameras},
     author = {Jiang, Meng and Liu, Zhou and Wang, Bishan and Yu, Lei and Yang, Wen},
     booktitle = {2020 IEEE International Conference on Image Processing (ICIP)},
     pages = {968--972},
     year = {2020}
   }

5. 

   Efficient Spatial-Temporal Normalization of SAE Representation for Event Camera

   Lin Shijie, Xu Fang, Wang Xuhong, Yang Wen, and Yu Lei

   IEEE Robotics and Automation Letters, 2020

   Bib Code

   @article{lin2020efficient,
     title = {Efficient Spatial-Temporal Normalization of SAE Representation for Event Camera},
     author = {Lin, Shijie and Xu, Fang and Wang, Xuhong and Yang, Wen and Yu, Lei},
     journal = {IEEE Robotics and Automation Letters},
     volume = {5},
     number = {3},
     pages = {4265--4272},
     year = {2020}
   }

6. 

   Event enhanced high-quality image recovery

   Wang Bishan, He Jingwei, Yu Lei ✉️, Xia Gui-Song, and Yang Wen

   In ECCV, 2020

   🔥 An approach to super-resolve blurry images with events.

   Bib HTML PDF Code

   @inproceedings{wang2020k,
     title = {Event enhanced high-quality image recovery},
     booktitle = {ECCV},
     author = {Wang, Bishan and He, Jingwei and Yu, Lei and Xia, Gui-Song and Yang, Wen},
     year = {2020},
     pages = {155--171}
   }

7. 

   Event-based high frame-rate video reconstruction with a novel cycle-event network

   Su Binyi, Yu Lei ✉️, and Yang Wen

   In 2020 IEEE International Conference on Image Processing (ICIP), 2020

   Bib

   @inproceedings{yu2020,
     title = {Event-based high frame-rate video reconstruction with a novel cycle-event network},
     booktitle = {2020 {IEEE} {International} {Conference} on {Image} {Processing} ({ICIP})},
     author = {Su, Binyi and Yu, Lei and Yang, Wen},
     year = {2020},
     pages = {86--90}
   }

8. Implicit Euler ODE networks for single-image dehazing

   Shen Jiawei, Li Zhuoyan, Yu Lei ✉️, Xia Gui-Song, and Yang Wen

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020

   Bib

   @inproceedings{shen2020,
     title = {Implicit {Euler} {ODE} networks for single-image dehazing},
     booktitle = {Proceedings of the {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} {Workshops}},
     author = {Shen, Jiawei and Li, Zhuoyan and Yu, Lei and Xia, Gui-Song and Yang, Wen},
     year = {2020},
     pages = {218--219}
   }

10. Facial Feature Embedded CycleGAN for VIS-NIR Translation

    Wang Huijiao, Zhang Haijian, Yu Lei, Wang Li, and Yang Xulei

    In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

    Bib

    @inproceedings{wang2020l,
      title = {Facial {Feature} {Embedded} {CycleGAN} for {VIS}-{NIR} {Translation}},
      booktitle = {{ICASSP} 2020-2020 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
      author = {Wang, Huijiao and Zhang, Haijian and Yu, Lei and Wang, Li and Yang, Xulei},
      year = {2020},
      pages = {1903--1907}
    }

11. Aim-Net: Bring Implicit Euler to Network Design

    Yuan Qiongwen, He Jingwei, Yu Lei ✉️, and Zheng Gang

    In 2020 IEEE International Conference on Image Processing (ICIP), 2020

    Bib

    @inproceedings{yuan2020,
      title = {Aim-{Net}: {Bring} {Implicit} {Euler} to {Network} {Design}},
      booktitle = {2020 {IEEE} {International} {Conference} on {Image} {Processing} ({ICIP})},
      author = {Yuan, Qiongwen and He, Jingwei and Yu, Lei and Zheng, Gang},
      year = {2020},
      pages = {1926--1930}
    }

12. Structured Bayesian learning for recovery of clustered sparse signal

    Wang Lu, Zhao Lifan, Yu Lei ✉️, Wang Jingjing, and Bi Guoan

    Signal Processing, 2020

    Abs Bib

    This paper considers the problem of recovering sparse signals with cluster structure of unknown sizes and locations. A hybrid prior is proposed by introducing a local continuity indicator, which adaptively imposes cluster information on the sparse coefficients according to the inherent data structure. The local continuity indicator flexibly switches the prior for a sparse coefficient between a fully pattern-coupled one and an independent one, so that the estimation of the sparse coefficient can selectively use the statistical information of its neighbors. Variational Bayesian inference is used to estimate the hidden variables based on the constructed probabilistic modeling. Numerical results of comprehensive simulations and real data experiments demonstrate that the proposed algorithm can effectively avoid the problem of structural mismatch and outperform other recently reported clustered sparse signal recovery algorithms in noisy environments.

    @article{wang2020,
      title = {Structured {Bayesian} learning for recovery of clustered sparse signal},
      volume = {166},
      journal = {Signal Processing},
      author = {Wang, Lu and Zhao, Lifan and Yu, Lei and Wang, Jingjing and Bi, Guoan},
      year = {2020},
      pages = {107255}
    }

2019

1. Algorithm to compute nonlinear partial observer normal form with multiple outputs

   Saadi Wided, Boutat Driss, Zheng Gang, Sbita Lassaad, and Yu Lei

   IEEE Transactions on Automatic Control, 2019

   🔥 TOP Journal

   Abs Bib

   It is well-known that observer design is a powerful tool to estimate the states of a dynamical system. Given a multi-output nonlinear dynamical system whose states are partially observable, this paper investigates the problem of observer design to estimate those observable states. It considers firstly a nonlinear system without inputs, and provides a set of geometric conditions, guaranteeing the existence of a change of coordinates which splits the studied nonlinear dynamical system into two subsystems, where one of them is of the well-known nonlinear observer normal form, for which a Luenberger-like observer can be designed. An extension to nonlinear systems with inputs has then been deduced.

   @article{saadi2019,
     title = {Algorithm to compute nonlinear partial observer normal form with multiple outputs},
     journal = {IEEE Transactions on Automatic Control},
     author = {Saadi, Wided and Boutat, Driss and Zheng, Gang and Sbita, Lassaad and Yu, Lei},
     year = {2019},
     pages = {1--1}
   }

2. 

   DET: A High-Resolution DVS Dataset for Lane Extraction

   Cheng Wensheng, Luo Hao, Yang Wen, Yu Lei, Chen Shoushun, and Li Wei

   In CVPR Workshops, 2019

   Abs Bib HTML Code

   Lane extraction is a basic yet necessary task for autonomous driving. Although past years have witnessed major advances in lane extraction with deep learning models, they all aim at ordinary RGB images generated by framebased cameras, which limits their performance in nature. To tackle this problem, we introduce Dynamic Vision Sensor (DVS), a type of event-based sensor to lane extraction task and build a high-resolution DVS dataset for lane extraction (DET). We collect the raw event data and generate 5,424 event-based sensor images with a resolution of 1280×800, the highest one among all DVS datasets available now. These images include complex traffic scenes and various lane types. All images of DET are annotated with multi-class segmentation format. The fully annotated DET images contains 17,103 lane instances, each of which is labeled pixel by pixel manually. We evaluate state-of-the-art lane extraction models on DET to build a benchmark for lane extraction task with event-based sensor images. Experimental results demonstrate that DET is quite challenging for even state-of-the-art lane extraction methods. DET is made publicly available, including the raw event data, accumulated images and labels1.

   @inproceedings{cheng2019,
     title = {{DET}: {A} {High}-{Resolution} {DVS} {Dataset} for {Lane} {Extraction}},
     booktitle = {CVPR Workshops},
     author = {Cheng, Wensheng and Luo, Hao and Yang, Wen and Yu, Lei and Chen, Shoushun and Li, Wei},
     year = {2019},
     pages = {10}
   }

3. Dynamical sparse signal recovery with fixed-time convergence

   Ren Junying, Yu Lei ✉️, Lyu Chengcheng, Zheng Gang, Barbot Jean-Pierre, and Sun Hong

   Signal Processing, 2019

   Abs Bib

   Arising in a large number of application areas, sparse recovery (SR) has been exhaustively investigated and many algorithms have been proposed. Different from the numerical methods realized by iterative algorithm, the recent continuous approach is realized through analog circuit, which takes advantage of short time-delay and fast convergence. However, the existing continuous method for SR still has the space to further improve the convergence speed. Consequently, in this paper, we propose a new dynamical continuous system to solve the sparse signal recovery problem with fixed-time convergence property.

   @article{ren2019c,
     title = {Dynamical sparse signal recovery with fixed-time convergence},
     volume = {162},
     journal = {Signal Processing},
     author = {Ren, Junying and Yu, Lei and Lyu, Chengcheng and Zheng, Gang and Barbot, Jean-Pierre and Sun, Hong},
     year = {2019},
     pages = {65--74}
   }

4. Image Denoising via Nonlocal Low Rank Approximation With Local Structure Preserving

   Liu Zhou, Yu Lei ✉️, and Sun Hong

   IEEE Access, 2019

   Abs Bib

   The nuclear norm minimization method emerged from a patch-based low-rank model leads to an excellent image denoising performance, where the non-local self-similarity over image patches is exploited. However, natural images are normally with complex and irregular image patches, which cannot be well represented using only a low-rank model, and thus most of them suffer from the over-penalty problem especially for images with lots of local irregular structures (e.g., fine details or sharp edges), and then results in over-smoothing problem after denoising. On the other hand, in order to represent the irregular components, edges defined over pixel level are often exploited. While the total variation (TV) is a well-known prior to remove noises and preserve edges, it might yield undesired staircase artifacts. The total generalized variation (TGV), a generalization of TV, can largely alleviate such staircase artifacts. Consequently, in order to deal with the over-smoothing problem aroused by a low-rank model, we propose a re-weighted TGV regularized nuclear norm minimization model for local structure preserving image denoising. Thanks to the split Bregman method, our proposed model can be effectively solved. A re-weighted strategy is developed to adaptively update the weight parameters of TGV regularization. The encouraging experimental results on noisy images demonstrate the effectiveness of our proposed method.

   @article{liu2019,
     title = {Image {Denoising} via {Nonlocal} {Low} {Rank} {Approximation} {With} {Local} {Structure} {Preserving}},
     volume = {7},
     journal = {IEEE Access},
     author = {Liu, Zhou and Yu, Lei and Sun, Hong},
     year = {2019},
     pages = {7117--7132}
   }

5. A Dynamical System With Fixed Convergence Time for Sparse Recovery

   Ren Junying, Yu Lei ✉️, Jiang Yulun, Barbot Jean-Pierre, and Sun Hong

   IEEE Access, 2019

   Abs Bib

   The sparse recovery (SR) algorithm, under the premise that signals are sparse, can be divided into two categories. One is a digital discrete method implemented via lots of iterative computations and the other is a continuous method implemented via analog circuits, which is usually faster. In this paper, we focus on the continuous method and propose a fixed-time convergence dynamical system. Compared with the existing system, it dynamically allocates the exponent according to time-varying elements of the system state, avoiding possible mismatches between the fixed exponent and some elements.

   @article{ren2019,
     title = {A {Dynamical} {System} {With} {Fixed} {Convergence} {Time} for {Sparse} {Recovery}},
     volume = {7},
     journal = {IEEE Access},
     author = {Ren, Junying and Yu, Lei and Jiang, Yulun and Barbot, Jean-Pierre and Sun, Hong},
     year = {2019},
     pages = {21844--21850}
   }

6. Block-sparsity recovery via recurrent neural network

   Lyu Chengcheng, Liu Zhou, and Yu Lei ✉️

   Signal Processing, 2019

   Bib

   @article{lyu2019,
     title = {Block-sparsity recovery via recurrent neural network},
     volume = {154},
     journal = {Signal Processing},
     author = {Lyu, Chengcheng and Liu, Zhou and Yu, Lei},
     year = {2019},
     pages = {129--135}
   }

8. 

   Live Demonstration: Real-Time Vi-SLAM With High-Resolution Event Camera

   Yang Gongyu, Ye Qilin, He Wanjun, Zhou Lifeng, Chen Xinyu, Yu Lei ✉️, Yang Wen, Chen Shoushun, and Li Wei

   In CVPR Workshops, 2019

   Abs Bib HTML Slides

   Event camera is bio-inspired vision sensors that output pixel-level brightness changes asynchronously. Compared to the conventional frame-based camera, it is with high dynamic range, low latency and high sensitivity, and thus can be exploited in SLAM to tackle the problem of occasions with high-speed camera moving and low-light scenes. In this demo, we implement the visual-inerial SLAM in real time with the recently released event camera, namely, CeleX-V. With the feature of high spatial resolution (1280×800) and low latency (\textless 0.5µs), the proposed method can provide frames with abundant textures and high time response, which leads to more stable tracking ability and better performance in SLAM system.

   @inproceedings{yang2019,
     title = {Live {Demonstration}: {Real}-{Time} {Vi}-{SLAM} {With} {High}-{Resolution} {Event} {Camera}},
     booktitle = {CVPR Workshops},
     author = {Yang, Gongyu and Ye, Qilin and He, Wanjun and Zhou, Lifeng and Chen, Xinyu and Yu, Lei and Yang, Wen and Chen, Shoushun and Li, Wei},
     year = {2019},
     pages = {2}
   }

2018

1. Image restoration via Bayesian dictionary learning with nonlocal structured beta process

   Liu Zhou, Yu Lei ✉️, and Sun Hong

   Journal of Visual Communication and Image Representation, 2018

   Bib

   @article{liu2018,
     title = {Image restoration via {Bayesian} dictionary learning with nonlocal structured beta process},
     volume = {52},
     journal = {Journal of Visual Communication and Image Representation},
     author = {Liu, Zhou and Yu, Lei and Sun, Hong},
     year = {2018},
     pages = {159--169}
   }

2. Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse Coding

   Zhang Menglei, Liu Zhou, and Yu Lei ✉️

   arXiv preprint arXiv:1812.11950, 2018

   Bib

   @article{zhang2018a,
     title = {Image {Super}-{Resolution} via {RL}-{CSC}: {When} {Residual} {Learning} {Meets} {Convolutional} {Sparse} {Coding}},
     journal = {arXiv preprint arXiv:1812.11950},
     author = {Zhang, Menglei and Liu, Zhou and Yu, Lei},
     year = {2018}
   }

2017

1. 

   Dynamical sparse recovery with finite-time convergence

   Yu Lei ✉️, Zheng Gang, and Barbot Jean-Pierre

   IEEE Transactions on Signal Processing, 2017

   🔥 TOP Journal. Recover sparse signals with analog circuits.

   Bib

   @article{yu2017,
     title = {Dynamical sparse recovery with finite-time convergence},
     volume = {65},
     number = {23},
     journal = {IEEE Transactions on Signal Processing},
     author = {Yu, Lei and Zheng, Gang and Barbot, Jean-Pierre},
     year = {2017},
     pages = {6146--6157}
   }

2. 

   Sparse Bayesian learning for image rectification with transform invariant low-rank textures

   Hu Shihui, Liu Zhou, Yu Lei ✉️, and Sun Hong

   Signal Processing, 2017

   Bib

   @article{hu2017,
     title = {Sparse {Bayesian} learning for image rectification with transform invariant low-rank textures},
     volume = {137},
     journal = {Signal Processing},
     author = {Hu, Shihui and Liu, Zhou and Yu, Lei and Sun, Hong},
     year = {2017},
     pages = {298--308}
   }

2015

1. 

   Model based Bayesian compressive sensing via local beta process

   Yu Lei ✉️, Sun Hong, Zheng Gang, and Barbot Jean Pierre

   Signal Processing, 2015

   Bib Code

   @article{yu2015,
     title = {Model based {Bayesian} compressive sensing via local beta process},
     volume = {108},
     journal = {Signal Processing},
     author = {Yu, Lei and Sun, Hong and Zheng, Gang and Barbot, Jean Pierre},
     year = {2015},
     pages = {259--271}
   }

2012

1. 

   Bayesian compressive sensing for cluster structured sparse signals

   Yu Lei ✉️, Sun Hong, Barbot Jean-Pierre, and Zheng Gang

   Signal processing, 2012

   🔥 A nonparametric approach to recover cluster / block sparse signals

   Bib HTML Code

   @article{yu2012,
     title = {Bayesian compressive sensing for cluster structured sparse signals},
     volume = {92},
     number = {1},
     journal = {Signal processing},
     author = {Yu, Lei and Sun, Hong and Barbot, Jean-Pierre and Zheng, Gang},
     year = {2012},
     pages = {259--269}
   }

2010

1. 

   Compressive sensing with chaotic sequence

   Yu Lei ✉️, Barbot Jean Pierre, Zheng Gang, and Sun Hong

   IEEE Signal Processing Letters, 2010

   🔥 A theoretical analysis of chaotic compressive sensing.

   Bib

   @article{yu2010f,
     title = {Compressive sensing with chaotic sequence},
     volume = {17},
     number = {8},
     journal = {IEEE Signal Processing Letters},
     author = {Yu, Lei and Barbot, Jean Pierre and Zheng, Gang and Sun, Hong},
     year = {2010},
     pages = {731--734}
   }

2. 

   Observability forms for switched systems with zeno phenomenon or high switching frequency

   Yu Lei ✉️, Barbot Jean-Pierre, Boutat Driss, and Benmerzouk Djamila

   IEEE Transactions on Automatic Control, 2010

   🔥 Top Journal

   Bib

   @article{yu2010g,
     title = {Observability forms for switched systems with zeno phenomenon or high switching frequency},
     volume = {56},
     number = {2},
     journal = {IEEE Transactions on Automatic Control},
     author = {Yu, Lei and Barbot, Jean-Pierre and Boutat, Driss and Benmerzouk, Djamila},
     year = {2010},
     pages = {436--441}
   }