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Nonlinear total variation based noise removal algorithms
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Cited by
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2662206 · Cited by 8,983
A Non-Local Algorithm for Image Denoising
Buades, A., Coll, B., & Morel, J.-M. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.38 · Cited by 7,616
Stable signal recovery from incomplete and inaccurate measurements
Candès, E. J., Romberg, J. K., & Tao, T. (2006) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20124 · Cited by 7,036
Sparse MRI: The application of compressed sensing for rapid MR imaging
Lustig, M., Donoho, D., & Pauly, J. M. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21391 · Cited by 6,952
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517384113 · Cited by 4,796
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
Chambolle, A., & Pock, T. (2011) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-010-0251-1 · Cited by 4,336
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
Cha, Y., Choi, W., & Büyüköztürk, O. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12263 · Cited by 3,295
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Bioucas-Dias, J. M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., & Chanussot, J. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2194696 · Cited by 2,836
Deep Convolutional Neural Network for Inverse Problems in Imaging
Jin, K. H., McCann, M. T., Froustey, E., & Unser, M. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2713099 · Cited by 2,529
“compressed sensing [7] and the related regularizers such as total variation [1], [2], [4], [8], robust and practical algorithms”
BackgroundDTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078
A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration
Bioucas-Dias, J. M., & Figueiredo, M. A. T. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.909319 · Cited by 2,064
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
Wang, Y., Yang, J., Yin, W., & Zhang, Y. (2008) · SIAM Journal on Imaging Sciences · doi:10.1137/080724265 · Cited by 1,975
Denoising of diffusion MRI using random matrix theory
Veraart, J., Novikov, D. S., Christiaens, D., Ades-aron, B., Sijbers, J., & Fieremans, E. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.016 · Cited by 1,905
An Iterative Regularization Method for Total Variation-Based Image Restoration
Osher, S., Burger, M., Goldfarb, D., Xu, J., & Yin, W. (2005) · Multiscale Modeling & Simulation · doi:10.1137/040605412 · Cited by 1,787
Self-citationA Duality Based Approach for Realtime TV-L 1 Optical Flow
Zach, C., Pock, T., & Bischof, H. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74936-3_22 · Cited by 1,658
Nonlocally Centralized Sparse Representation for Image Restoration
Dong, W., Zhang, L., Shi, G., & Li, X. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2235847 · Cited by 1,505
Zhu, L., & Han, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_2 · Cited by 1,906
Chen, Y., & Pock, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2596743 · Cited by 1,344
Fast Image Recovery Using Variable Splitting and Constrained Optimization
Afonso, M. V., Bioucas-Dias, J. M., & Figueiredo, M. A. T. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2047910 · Cited by 1,223
Plug-and-Play priors for model based reconstruction
Venkatakrishnan, S. V., Bouman, C. A., & Wohlberg, B. (2013) · 2013 IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2013.6737048 · Cited by 1,178
Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., Maaten, L. V. D., Yuille, A. L., & He, K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00059 · Cited by 940
The Little Engine That Could: Regularization by Denoising (RED)
Romano, Y., Elad, M., & Milanfar, P. (2017) · SIAM Journal on Imaging Sciences · doi:10.1137/16m1102884 · Cited by 934
Image super-resolution: the techniques, applications, and future
Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., & Zhang, L. (2016) · Signal Processing · doi:10.1016/j.sigpro.2016.05.002 · Cited by 554
Inferring biological networks by sparse identification of nonlinear dynamics
Mangan, N. M., Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016) · IEEE Transactions on Molecular, Biological and Multi-Scale Communications · doi:10.1109/tmbmc.2016.2633265 · Cited by 436
Handbook of Quantile Regression
(2017) · doi:10.1201/9781315120256 · Cited by 392
REMODE: Probabilistic, monocular dense reconstruction in real time
Pizzoli, M., Forster, C., & Scaramuzza, D. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6907233 · Cited by 330
FlexISP: a flexible camera image processing framework
Heide, F., Steinberger, M., Tsai, Y.-T., Rouf, M., Pająk, D., Reddy, D., Gallo, O., Liu, J., Heidrich, W., Egiazarian, K., et al. (2014) · ACM Transactions on Graphics · doi:10.1145/2661229.2661260 · Cited by 298
Model inversion attacks against collaborative inference
He, Z., Zhang, T., & Lee, R. B. (2019) · Proceedings of the 35th Annual Computer Security Applications Conference · doi:10.1145/3359789.3359824 · Cited by 313
Total Variation Blind Deconvolution: The Devil Is in the Details
Perrone, D., & Favaro, P. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.372 · Cited by 280
Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection
Cheng, T., & Wang, B. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2936609 · Cited by 266
A Fast Total Variation Minimization Method for Image Restoration
Huang, Y., Ng, M. K., & Wen, Y.-W. (2008) · Multiscale Modeling & Simulation · doi:10.1137/070703533 · Cited by 218
Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video
Garg, R., Roussos, A., & Agapito, L. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.168 · Cited by 210
Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation
Munda, G., Reinbacher, C., & Pock, T. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1106-2 · Cited by 181
SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture
Fan, X., Zhang, W., Zhang, C., Chen, A., & An, F. (2022) · Energy · doi:10.1016/j.energy.2022.124612 · Cited by 180
Robust Multichannel Blind Deconvolution via Fast Alternating Minimization
Sroubek, F., & Milanfar, P. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2175740 · Cited by 180
Okolie, C. J., & Smit, J. L. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.03.016 · Cited by 139
Combining sparsity and rotational invariance in EEG/MEG source reconstruction
Haufe, S., Nikulin, V. V., Ziehe, A., Müller, K.-R., & Nolte, G. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.04.246 · Cited by 130
(2013) · Liverpool John Moores University · Cited by 133
Human activity recognition using wearable sensors by heterogeneous convolutional neural networks
Han, C., Zhang, L., Tang, Y., Huang, W., Min, F., & He, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116764 · Cited by 130
Recognizing Facial Expressions Using a Shallow Convolutional Neural Network
Miao, S., Xu, H., Han, Z., & Zhu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2921220 · Cited by 110
A Clearer Picture of Total Variation Blind Deconvolution
Perrone, D., & Favaro, P. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2477819 · Cited by 109
Turning a denoiser into a super-resolver using plug and play priors
Brifman, A., Romano, Y., & Elad, M. (2016) · 2016 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2016.7532589 · Cited by 91
Sohrabpour, A., Lu, Y., Worrell, G., & He, B. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.064 · Cited by 89
“The notion of edge sparsity or imposing sparsity on the gradient of the solution is also referred to as the total-variation (TV) in image processing literature (Adde et al. 2005; Rudin et al., 1992).”
BackgroundOptimal Control Formulation for Determining Optical Flow
Borzì, A., Ito, K., & Kunisch, K. (2003) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827501386481 · Cited by 82
Duality TV-L1 flow with fundamental matrix prior
Wedel, A., Pock, T., Braun, J., Franke, U., & Cremers, D. (2008) · 2008 23rd International Conference Image and Vision Computing New Zealand · doi:10.1109/ivcnz.2008.4762119 · Cited by 58
Security and Privacy Issues in Deep Learning
Bae, H., Jang, J., Jung, D., Jang, H., Ha, H., Lee, H., & Yoon, S. (2018) · arXiv · doi:10.48550/arxiv.1807.11655 · Cited by 56
Moiré pattern removal from texture images via low-rank and sparse matrix decomposition
Liu, F., Yang, J., & Yue, H. (2015) · 2015 Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip.2015.7457907 · Cited by 56
Introduction to variational image-processing models and applications
Chen, K. (2013) · International Journal of Computer Mathematics · doi:10.1080/00207160.2012.757073 · Cited by 53
Local spatial constraint and total variation for hyperspectral anomaly detection
Feng, R., Li, H., Wang, L., Zhong, Y., Zhang, L., & Zeng, T. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3098814 · Cited by 46
Gai, J., Obeid, N., Holtrop, J. L., Wu, X.-L., Lam, F., Fu, M., Haldar, J. P., Hwu, W.-M. W., Liang, Z.-P., & Sutton, B. P. (2013) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2013.01.001 · Cited by 43
Representer Theorems for Sparsity-Promoting $\ell _{1}$ Regularization
Unser, M., Fageot, J., & Gupta, H. (2016) · IEEE Transactions on Information Theory · doi:10.1109/tit.2016.2590421 · Cited by 36
Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey
Xu, L., Zhu, S., & Wen, N. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac9cb3 · Cited by 35
Identification of nonlinear dynamical systems with time delay
Leylaz, G., Wang, S., & Sun, J.-Q. (2022) · International Journal of Dynamics and Control · doi:10.1007/s40435-021-00783-7 · Cited by 35
DIPPAS: a deep image prior PRNU anonymization scheme
Picetti, F., Mandelli, S., Bestagini, P., Lipari, V., & Tubaro, S. (2022) · EURASIP Journal on Information Security · doi:10.1186/s13635-022-00128-7 · Cited by 26
Removal of adherent noises from image sequences by spatio-temporal image processing
Yamashita, A., Fukuchi, I., Kaneko, T., & Miura, K. T. (2008) · 2008 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2008.4543570 · Cited by 21
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps
Dao, T., Sohoni, N. S., Gu, A., Eichhorn, M., Blonder, A., Leszczynski, M., Rudra, A., & Ré, C. (2020) · arXiv · doi:10.48550/arxiv.2012.14966 · Cited by 21
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Padcharoen, A., Kitkuan, D., Kumam, P., Rilwan, J., & Kumam, W. (2020) · Inverse Problems in Science and Engineering · doi:10.1080/17415977.2019.1709454 · Cited by 17
Fast Weighted Total Variation Regularization Algorithm for Blur Identification and Image Restoration
Liu, H., Gu, J., Meng, M. Q.-H., & Lu, W.-S. (2016) · IEEE Access · doi:10.1109/access.2016.2516949 · Cited by 16
Sparsity-Aware Learning and Compressed Sensing: An Overview
Theodoridis, S., Kopsinis, Y., & Slavakis, K. (2014) · Academic Press Library in Signal Processing · doi:10.1016/b978-0-12-396502-8.00023-1 · Cited by 14
Magnetic imaging and machine vision NDT for the on-line inspection of stainless steel strips
Ricci, M., Ficola, A., Fravolini, M. L., Battaglini, L., Palazzi, A., Burrascano, P., Valigi, P., Appolloni, L., Cervo, S., & Rocchi, C. (2013) · Measurement Science and Technology · doi:10.1088/0957-0233/24/2/025401 · Cited by 11
Defective samples simulation through neural style transfer for automatic surface defect segment
Wei, T., Cao, D., Jiang, X., Zheng, C., & Liu, L. (2020) · 2019 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems · doi:10.1117/12.2540464 · Cited by 10
“Using tv loss can increase the smoothness of the transitions between specified transferred region and background 8,21 , making the composite region less abrupt.”
Background
Metadata
- Authors
- Rudin, L. I., Osher, S., & Fatemi, E.
- Venue
- Physica D: Nonlinear Phenomena
- Publisher
- Elsevier BV
- Published
- 1992-11
- Volume
- 60
- Issue
- 1-4
- Pages
- 259-268
- ISSN
- 0167-2789 (ISSN-L)
- OpenAlex
- W2103559027
- Cited by
- 15,704
- References
- 0
- FWCI
- 7.94
- Type
- journal article
- Language
- en
- Open access
- closed
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