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Robust Multi-image Processing with Optimal Sparse Regularization

Yann Traonmilin, Saïd Ladjal, Andrés Almansa
2014 Journal of Mathematical Imaging and Vision  
Finally, we show that in the case of multi-image processing, the structure of the support of signal and noise must be studied precisely.  ...  In this article, we review and extend results of the literature to the robustness to outliers of overdetermined signal recovery problems under sparse regularization, with a convex variational formulation  ...  Finally, in Section 5, we study the benefits of sparse regularization for forgiveness in the area of multi-image processing.  ... 
doi:10.1007/s10851-014-0532-1 fatcat:ylvl5pielnexppegzswxpfuiwy

Optical flow estimation using learned sparse model

Kui Jia, Xiaogang Wang, Xiaoou Tang
2011 2011 International Conference on Computer Vision  
In particular, our method is based on multi-scale spatial regularization, which benefits from first-order spatial regularity and our learned, higher order sparse model.  ...  Moreover, as the errors in intermediate flow estimates are usually dense with large variations, we further propose flow-driven and image-driven approaches to address the problem of outliers.  ...  We propose multi-scale spatial regularization and a sequential optimization scheme.  ... 
doi:10.1109/iccv.2011.6126522 dblp:conf/iccv/JiaWT11 fatcat:vjg4xhecprfezizjsrg2rkx3ym

[SAM 2020 Title Page]

2020 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)  
Monopulse Processing for Multiple Observations with Applications to TS-MIMO Radar 136 1570620053 Robust Coexistence Design of MIMO Radar and MIMO Communication under Model Uncertainty 137 1570617712  ...  sources with unknown mutual coupling 88 1570621214 Enhancement 116 1570622673 Online Robust Reduced-Rank Regression 117 1570618306 Optimization Inspired Learning Network for Multiuser Robust Beamforming  ... 
doi:10.1109/sam48682.2020.9104267 fatcat:erntqdmhdrdspcrkvjowtplyyq

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 5310-5323 Image Restoration by Combined Order Regularization With Optimal Spatial Adaptation.  ...  ., +, TIP 2020 44-56 Image Restoration by Combined Order Regularization With Optimal Spatial Adaptation.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

2020 Index IEEE Transactions on Computational Imaging Vol. 6

2020 IEEE Transactions on Computational Imaging  
., +, TCI 2020 153-166 SISTER: Spectral-Image Similarity-Based Tensor With Enhanced-Sparsity Reconstruction for Sparse-View Multi-Energy CT.  ...  Tachella, J., +, TCI 2020 208-220 Robust Restoration of Sparse Multidimensional Single-Photon LiDAR Images.  ... 
doi:10.1109/tci.2021.3054596 fatcat:puij7ztll5ai7alxrmqzsupcny

Table of contents

2020 IEEE transactions on circuits and systems for video technology (Print)  
Zhao 2329 Reversible Data Hiding in JPEG Images With Multi-Objective Optimization ................. Z. Yin, Y. Ji, and B.  ...  Pei 2406 REGULAR PAPERS Image/Video Processing Detail-Enhanced Multi-Scale Exposure Fusion in YUV Color Space ............ Q. Wang, W. Chen, X. Wu, and Z.  ... 
doi:10.1109/tcsvt.2020.3005583 fatcat:fzrumcitirg3tkr3yltcedm3ti

Improved Sparse Coding Algorithm with Device-Free Localization Technique for Intrusion Detection and Monitoring

Huakun Huang, Zhaoyang Han, Shuxue Ding, Chunhua Su, Lingjun Zhao
2019 Symmetry  
Contrasting to the previous works, we exploit the l 2 , 1 norm as the regularizer and devise an efficient optimization method with a proximal operator-based scheme, which leads the proposed improved-sparse-coding  ...  Compared with the state-of-the-art methods that adopt l 1 norm as the regularizer, the proposed algorithm can improve the joint sparsity of sparse solution.  ...  For the process of sparse coding, different regularizer will generate different sparse patterns.  ... 
doi:10.3390/sym11050637 fatcat:7zylsn2qxrbdbgc7qy4x6jtdzm

Face Recognition Based on Multi-classifierWeighted Optimization and Sparse Representation

Deng Nan, Zhengguang Xu, ShengQin Bian
2013 International Journal of Signal Processing, Image Processing and Pattern Recognition  
According to the multiple voting results of the classifiers, the weights of multi-classifiers are optimized by a least-squares optimization equation with 2 l -norm regularization.  ...  Pengfei Zhu1 et al., [17] proposed a multi-scale Patch based Collaborative Representation for Face Recognition with Margin Distribution Optimization sparse representation model for face recognition.  ...  Sparse Representation based Multi-Classifier Weighted Optimization and Fusion for FR We first introduce the overall process of the multi-classifier weighted optimization and sparse representation based  ... 
doi:10.14257/ijsip.2013.6.5.37 fatcat:nvw4su752neytdkvbuif2upmby

SAM 2020 Author Index

2020 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)  
Against Channel Errors R04.3 GPU-accelerated parallel optimization for sparse regularization Pu, Wenqiang R05.6 Beam Pattern Synthesis for Conformal Array with Sidelobe and Polarization Control  ...  optimization for sparse regularization Liu, Wei SS01.1 A general ESPRIT method for noncircularity-based incoherently distributed sources SS01.3 DOA Estimation for Coexistence of Circular and  ... 
doi:10.1109/sam48682.2020.9104397 fatcat:cfp5gsikrzabhhcnkalahjkxze

A prior image constraint robust principal component analysis reconstruction method for sparse segmental multi-energy computed tomography

Bin Li, Ning Luo, Anni Zhong, Yongbao Li, Along Chen, Linghong Zhou, Yuan Xu
2021 Quantitative Imaging in Medicine and Surgery  
This scan only needs to switch tube voltage a few times to acquire multi-energy data, but leads to sparse-view and limited-angle issues in image reconstruction.  ...  Multi-energy computed tomography (MECT) is a promising technique in medical imaging, especially for quantitative imaging.  ...  Funding: This work is supported in part by Guangdong Basic and Applied Basic Research Foundation (2020A1515110352), Open Access Statement: This is an Open Access article distributed in accordance with  ... 
doi:10.21037/qims-20-844 pmid:34476191 pmcid:PMC8339662 fatcat:6d6p7mh5rvf3fn446kmbhrs33m

2019 Index IEEE Transactions on Computational Imaging Vol. 5

2019 IEEE Transactions on Computational Imaging  
., +, TCI Sept. 2019 344-353 Stereo image processing Deep Visual Sharing With Colorblind.  ...  Mignard-Debise, L., +, TCI Dec. 2019 585-595 Optimization Joint SAR Imaging and Multi-Feature Decomposition From 2-D Under- Sampled Data Via Low-Rankness Plus Sparsity Priors.  ...  Surgery Multiresolution Cube Propagation for 3-D Ultrasound Image Reconstruction. Dong  ... 
doi:10.1109/tci.2019.2959176 fatcat:g7nuyesverg2xbjwbzuyp6ovyy

Robust Cardiac Motion Estimation With Dictionary Learning and Temporal Regularization for Ultrasound Imaging

N. Ouzir, J. Bioucas-Dias, A. Basarab, J.-Y. Tourneret
2019 2019 IEEE International Ultrasonics Symposium (IUS)  
cardiac motion estimation with dictionary learning and temporal regularization for ultrasound imaging.  ...  Abstract-Estimating the cardiac motion from ultrasound (US) images is an ill-posed problem that requires regularization.  ...  CONCLUSIONS This paper introduced a new multi-frame approach with sparse and temporal regularizations for cardiac motion estimation in US images.  ... 
doi:10.1109/ultsym.2019.8925936 fatcat:egczq4rp5bdptcqeo4kgitawfy

Sparse multi-stage regularized feature learning for robust face recognition

Mohamed Anouar Borgi, Demetrio Labate, Maher El Arbi, Chokri Ben Amar
2015 Expert systems with applications  
We provide extensive numerical tests to show that our Sparse Multi-Regularized Shearlet Network (SMRSN) algorithm performs very competitively when compared against different state-of-the-art methods on  ...  Second, we apply a refinement of the Multi-Task Sparse Learning (MTSL) framework to exploit the relationships among multiple shared tasks generated by changing the regularization parameter during the recognition  ...  CONCLUSION AND FUTURE WORK This paper presents a novel for robust face recognition method called Sparse Multi-Regularized Shearlet Network (SMRSN).  ... 
doi:10.1016/j.eswa.2014.07.044 fatcat:jiemiqumq5gqxpmrws3fhbxuoy

Sparse representation based blind image deblurring

Haichao Zhang, Jianchao Yang, Yanning Zhang, Thomas S. Huang
2011 2011 IEEE International Conference on Multimedia and Expo  
We propose a sparse representation based blind image deblurring method.  ...  By incorporating this prior into the deblurring process, we can effectively regularize the illposed inverse problem and alleviate the undesirable ring effect which is usually suffered by conventional deblurring  ...  to stabilize the blur kernel estimation.The proposed model can be optimized efficiently with the recent progress of sparse optimization techniques.  ... 
doi:10.1109/icme.2011.6012035 dblp:conf/icmcs/ZhangYZH11 fatcat:5klyho67yzfwjgrwljhtzmnae4

Multi-view multi-sparsity kernel reconstruction for multi-class image classification

Xiaofeng Zhu, Qing Xie, Yonghua Zhu, Xingyi Liu, Shichao Zhang
2015 Neurocomputing  
This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model.  ...  Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS  ...  , separable sparse learning needs to perform its optimization process four times.  ... 
doi:10.1016/j.neucom.2014.08.106 fatcat:4ouieljmvzddzcdjvu35sfsida
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