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1. Full Text Revisiting Bayesian blind deconvolution
by Wipf, David and Zhang, Haichao
Journal of Machine Learning Research, ISSN 1532-4435, 01/2015, Volume 15, pp. 3595 - 3634
Blind deconvolution | Sparse priors | Sparse estimation | Variational Bayes | Blind image deblurring
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2. Full Text Revisiting Bayesian Blind Deconvolution
by Wipf, D and Zhang, HC
JOURNAL OF MACHINE LEARNING RESEARCH, ISSN 1532-4435, 11/2014, Volume 15, pp. 3595 - 3634
Blind deconvolution involves the estimation of a sharp signal or image given only a blurry observation. Because this problem is fundamentally ill-posed, strong... 
sparse estimation | blind image deblurring | sparse priors | variational Bayes | ALGORITHMS | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | blind deconvolution
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3. Full Text Sparse Bayesian Learning-Based Time-Variant Deconvolution
by Sanyi Yuan and Shangxu Wang and Ming Ma and Yongzhen Ji and Li Deng
IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, 11/2017, Volume 55, Issue 11, pp. 6182 - 6194
In seismic exploration, the wavelet-filtering effect and Q-filtering (amplitude attenuation and velocity dispersion) effect blur the reflection image of... 
Earth | Time-frequency analysis | thin bed | Deconvolution | Convolution | sparse Bayesian learning (SBL) | sparse representations | Bayesian framework | inverse problems | Attenuation | Bayes methods | Dispersion | deconvolution | WAVELET ESTIMATION | REFLECTIVITY | CLASSIFICATION | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | INVERSION | NONSTATIONARY DECONVOLUTION | ENGINEERING, ELECTRICAL & ELECTRONIC | GEOCHEMISTRY & GEOPHYSICS | REMOTE SENSING | ABSORPTION-COMPENSATION | Traveltime | Wave attenuation | Filtration | Methodology | Probability theory | Imaging techniques | Exploration | Seismic exploration | Remote sensing | Wavelet | Image quality | Artifacts | Reflectors | Clonal deletion | Seismograms | Capacity | Quality | Machine learning | Deletion | Regularization | Bayesian analysis | Reflectance
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4. Full Text Coded time of flight cameras
: sparse deconvolution to address multipath interference and recover time profiles
by Kadambi, Achuta and Whyte, Refael and Bhandari, Ayush and Streeter, Lee and Barsi, Christopher and Dorrington, Adrian and Raskar, Ramesh
ACM Transactions on Graphics (TOG), ISSN 0730-0301, 11/2013, Volume 32, Issue 6, pp. 1 - 10
Time of flight cameras produce real-time range maps at a relatively low cost using continuous wave amplitude modulation and demodulation. However, they are... 
femtophotography | multipath interference | time-coded illumination | time of flight (ToF) cameras | sparse deconvolution | Sparse deconvolution | Time-coded illumination | Multipath interference | Femtophotography | Time of flight (ToF) cameras | Time of Flight (ToF) cameras | COMPUTER SCIENCE, SOFTWARE ENGINEERING | LIGHT | NORM | SCATTERING | Continuous wave | Maps | Systematic errors | Low cost | Interference | Cameras | Real time | Demodulation
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5. Full Text Band-controlled sparse deconvolution
by Du, Xin and Li, Guofa and Zhou, Zhenxiao and Zhang, Wenbo and Tang, Bowen
Journal of Applied Geophysics, ISSN 0926-9851, 08/2018, Volume 155, pp. 53 - 61
Sparse-spike deconvolution is widely applied for improving the resolution of stacked seismogram. However, the method frequently suffers from instability and... 
Deconvolution | Sparse | Reliability | Controlled bandwidth | GEOSCIENCES, MULTIDISCIPLINARY | RECONSTRUCTION | MINING & MINERAL PROCESSING | DECOMPOSITION | SEISMIC DATA | INVERSION | Analysis | Algorithms
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6. Full Text Multichannel Sparse Blind Deconvolution on the Sphere
by Li, Yanjun and Bresler, Yoram
IEEE Transactions on Information Theory, ISSN 0018-9448, 11/2019, Volume 65, Issue 11, pp. 7415 - 7436
Multichannel blind deconvolution is the problem of recovering an unknown signal f and... 
Riemannian gradient | Linear programming | nonconvex optimization | Sparse matrices | super-resolution fluorescence microscopy | Manifold gradient descent | Manifolds | Riemannian Hessian | Deconvolution | Convolution | Microscopy | strict saddle points | Sensors
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7. Full Text From Bernoulli-Gaussian Deconvolution to Sparse Signal Restoration
by Soussen, C and Idier, J and Brie, D and Duan, J
IEEE Transactions on Signal Processing, ISSN 1053-587X, 10/2011, Volume 59, Issue 10, pp. 4572 - 4584
Formulated as a least square problem under an l 0 constraint, sparse signal restoration is a discrete optimization problem, known to be NP complete. Classical... 
SMLR algorithm | Dictionaries | Matching pursuit algorithms | stepwise regression algorithms | Minimization | Least squares approximation | Bernoulli-Gaussian (BG) signal restoration | Signal restoration | sparse signal estimation | Signal processing algorithms | inverse problems | orthogonal least squares | mixed ell_2 - ell_0 criterion minimization | mixed ℓ | criterion minimization | SUBSET-SELECTION | RECONSTRUCTION | BACKWARD GREEDY ALGORITHM | mixed l-l criterion minimization | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal processing | Usage | Mathematical optimization | Simulation methods | Innovations | Gaussian processes | Studies | Algorithms | Matching | Restoration | Least squares method | Searching | Regression | Computing time | Optimization | Engineering Sciences | Automatic
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8. Full Text Leveraging Diversity and Sparsity in Blind Deconvolution
by Ahmed, Ali and Demanet, Laurent
IEEE Transactions on Information Theory, ISSN 0018-9448, 06/2018, Volume 64, Issue 6, pp. 3975 - 4000
This paper considers recovering L -dimensional vectors Convolutional codes | compressed sensing | multichannel | rank-1 matrix | Discrete Fourier transforms | Minimization | image deblurring | convex programming | Blind deconvolution | Sparse matrices | nuclear norm minimization | Optimization | Standards | Deconvolution | matrix factorizations | channel estimation | passive sensing | low-rank matrix | Wireless communications | Messages | Subspaces | Impulse response
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9. Full Text MPTV: Matching Pursuit-Based Total Variation Minimization for Image Deconvolution
by Dong Gong and Mingkui Tan and Qinfeng Shi and van den Hengel, Anton and Yanning Zhang
IEEE Transactions on Image Processing, ISSN 1057-7149, 04/2019, Volume 28, Issue 4, pp. 1851 - 1865
Total variation (TV) regularization has proven effective for a range of computer vision tasks through its preferential weighting of sharp image edges. Existing... 
Deconvolution | Matching pursuit algorithms | Total variation | Minimization | convex programming | matching pursuit | Task analysis | Kernel | Optimization | image deconvolution | ALGORITHM | BLIND DECONVOLUTION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | SPARSE REPRESENTATION | MODELS | SPLIT BREGMAN METHOD | REGULARIZATION | SELECTION | Computer vision | Artifacts | State of the art | Model matching | Smoothing | Images | Linear programming | Inhomogeneity | Parameter sensitivity | Regularization | Computer Science - Computer Vision and Pattern Recognition
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10. Full Text Blind Deconvolution From Multiple Sparse Inputs
by Liming Wang and Yuejie Chi
IEEE Signal Processing Letters, ISSN 1070-9908, 10/2016, Volume 23, Issue 10, pp. 1384 - 1388
Blind deconvolution is an inverse problem when both the input signal and the convolution kernel are unknown. We propose a convex algorithm based on... 
Deconvolution | Dictionaries | Convolution | Blind calibration | Signal processing algorithms | convex programming | sparsity | Calibration | dictionary learning | Sparse matrices | Kernel | blind deconvolution | Sparsity | CALIBRATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Inverse problems | Mathematical analysis | Showcases | Mathematical models | Vectors (mathematics) | Blinds
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11. Full Text Deconvolution in Astronomy: A Review
by J. L. Starck and E. Pantin and F. Murtagh
Publications of the Astronomical Society of the Pacific, ISSN 0004-6280, 10/2002, Volume 114, Issue 800, pp. 1051 - 1069
This article reviews different deconvolution methods. The all‐pervasive presence of noise is what makes deconvolution particularly difficult. The diversity of... 
Information retrieval noise | Image resolution | Astronomical objects | Hubble Space Telescope | Signal noise | Wavelet analysis | Fourier transformations | Entropy | Review | Pixels | Astronomy | MAXIMUM-ENTROPY | EDGE-DETECTION | IMAGE-RESTORATION | RECONSTRUCTION | ASTRONOMY & ASTROPHYSICS | ADAPTIVE OPTICS | RESOLUTION | WAVELET TRANSFORM | MU-M | REGULARIZATION | SPARSE SPIKE TRAINS
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12. Full Text Nonstationary blind deconvolution of seismic records
by Haghshenas Lari, Hojjat and Gholami, Ali
Geophysics, ISSN 0016-8033, 01/2019, Volume 84, Issue 1, pp. V1 - V9
Seismic deconvolution used for improving the bandwidth of data is inherently nonstationary, mixed phase, and blind. Due to some restricting assumptions imposed... 
Sparse | Blind deconvolution | GEOCHEMISTRY & GEOPHYSICS | DOMAIN | REFLECTIVITY
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13. Full Text Seismic deconvolution and inversion with erratic data
by Dai, Ronghuo and Yin, Cheng and Yang, Shasha and Zhang, Fanchang
Geophysical Prospecting, ISSN 0016-8025, 11/2018, Volume 66, Issue 9, pp. 1684 - 1701
ABSTRACT If there are some erratic data (e.g. outliers), which may arise from measurement error, or other reasons, in seismic data, the seismic deconvolution... 
Total variation | Inversion | Erratic data | Sparse signal representation | Seismic deconvolution | PRINCIPLE PHASE-DECOMPOSITION | SPARSE | ALGORITHMS | GEOCHEMISTRY & GEOPHYSICS | SHRINKAGE | GAS | AVO | IMPEDANCE INVERSION | Error analysis | Methodology | Wavelet | Deconvolution | Inverse problems | Robustness (mathematics) | Outliers (statistics) | Seismology | Seismic surveys | Constraint modelling | Seismic studies | Representations | Computing time | Regularization | Seismic data
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14. Full Text Multichannel Semi-blind Deconvolution (MSBD) of seismic signals
by Mirel, Merabi and Cohen, Israel
Signal Processing, ISSN 0165-1684, 06/2017, Volume 135, pp. 253 - 262
Seismic deconvolution is a general problem associated with recovering the reflectivity series from a seismic signal when the wavelet is known. In this paper,... 
Wavelet estimation | Sparse deconvolution | Semi-blind | Multichannel deconvolution | Reflectivity recovery | Seismic deconvolution | BASIS PURSUIT | DECOMPOSITION | INVERSION | ENGINEERING, ELECTRICAL & ELECTRONIC | Analysis | Algorithms | Machine learning
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15. Full Text Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning
by Fang, Ruogu and Chen, Tsuhan and Sanelli, Pina C
Medical Image Analysis, ISSN 1361-8415, 05/2013, Volume 17, Issue 4, pp. 417 - 428
► Dictionaries are learned from high-dose CTP data for low-dose CBF estimation. ► Temporal convolution model is combined with spatial dictionary mapping prior.... 
Deconvolution algorithm | Sparse representation | Computed tomography perfusion | Online dictionary learning | Radiation dosage | STATISTICAL NOISE | RESIDUE FUNCTION | MRI | ENGINEERING, BIOMEDICAL | QUANTIFICATION | RECONSTRUCTION | TRACER BOLUS PASSAGES | DECOMPOSITION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | HIGH-RESOLUTION MEASUREMENT | CEREBRAL-BLOOD-FLOW | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | BRAIN | Cerebrovascular Disorders - physiopathology | Brain - diagnostic imaging | Reproducibility of Results | Information Storage and Retrieval - methods | Artificial Intelligence | Brain - physiopathology | Humans | Perfusion Imaging - methods | Tomography, X-Ray Computed - methods | Cerebrovascular Circulation | Radiation Dosage | Cerebral Angiography - methods | Online Systems | Radiation Protection | Dictionaries, Medical | Sensitivity and Specificity | Cerebrovascular Disorders - diagnostic imaging | Pattern Recognition, Automated - methods | Databases, Factual | CT imaging | Nuclear radiation | Algorithms | online dictionary learning | radiation dosage | sparse representation | deconvolution algorithm
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16. Full Text An iterative sparse deconvolution method for simultaneous multicolor 19F‐MRI of multiple contrast agents
by Schoormans, Jasper and Calcagno, Claudia and Daal, Mariah R.R and Wüst, Rob C.I and Faries, Christopher and Maier, Alexander and Teunissen, Abraham J.P and Naidu, Sonum and Sanchez‐Gaytan, Brenda L and Nederveen, Aart J and Fayad, Zahi A and Mulder, Willem J.M and Coolen, Bram F and Strijkers, Gustav J
Magnetic Resonance in Medicine, ISSN 0740-3194, 01/2020, Volume 83, Issue 1, pp. 228 - 239
Purpose 19F‐MRI is gaining widespread interest for cell tracking and quantification of immune and inflammatory cells in vivo. Different fluorinated compounds... 
deconvolution | compressed sensing | multiplex | sparse MRI | fluorine MRI | multicolor | 19F | Animal experimentation | Numerical analysis | Fluorides | Methods | Noise levels | Separation | Oils | Computer simulation | Fluorine | Noise | Inflammation | Injection | Chemical compounds | Artifacts | Organic chemistry | Contrast agents | Chemical equilibrium | Deconvolution | Magnetic resonance imaging | Simulation | Robustness (mathematics) | Mathematical models | Iterative methods | Fluorination | Probes | Contrast media | System effectiveness
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17. Subspace-based non-blind deconvolution
by Zhuang, PX and Ding, XH and Duan, JM
MATHEMATICAL BIOSCIENCES AND ENGINEERING, ISSN 1547-1063, 2019, Volume 16, Issue 4, pp. 2202 - 2218
In this paper, we develop a novel subspace-based recovery algorithm for non-blind deconvolution (named SND). With considering visual importance difference... 
subspace fidelity | SPARSE REPRESENTATION | IMAGE | FUSION | subspace prior | non-blind deconvolution | ALGORITHM | MATHEMATICAL & COMPUTATIONAL BIOLOGY | least square integration | fast Fourier transform
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18. Full Text Identifiability in Blind Deconvolution With Subspace or Sparsity Constraints
by Yanjun Li and Kiryung Lee and Bresler, Yoram
IEEE Transactions on Information Theory, ISSN 0018-9448, 07/2016, Volume 62, Issue 7, pp. 4266 - 4275
Blind deconvolution (BD), the resolution of a signal and a filter given their convolution, arises in many applications. Without further constraints, BD is... 
channel identification | multipath channel | equalization | Deconvolution | Dictionaries | Convolution | bilinear inverse problem | Subspace constraints | uniqueness | Complexity theory | Sparse matrices | Uniqueness | COMPUTER SCIENCE, INFORMATION SYSTEMS | ENGINEERING, ELECTRICAL & ELECTRONIC | Convolutional codes | Usage | Algebra | Chaos theory | Analysis | Mathematical analysis | Vectors (mathematics) | Subspaces | Blinds | Optimization | Complexity
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19. Full Text Sparse Deconvolution Methods for Ultrasonic NDT
: Application on TOFD and Wall Thickness Measurements
by Boßmann, Florian and Plonka, Gerlind and Peter, Thomas and Nemitz, Oliver and Schmitte, Till
Journal of Nondestructive Evaluation, ISSN 0195-9298, 9/2012, Volume 31, Issue 3, pp. 225 - 244
In this work we present two sparse deconvolution methods for nondestructive testing. The first method is a special matching pursuit (MP) algorithm in order to... 
Orthogonal matching pursuit | Engineering | Vibration, Dynamical Systems, Control | Sparse blind deconvolution | Parameter estimation | Approximate Prony method | Matching pursuit | Mechanics | Characterization and Evaluation of Materials | Sparse representation | Time of flight diffraction | Structural Mechanics | B-SCAN IMAGES | ECHOES | APPROXIMATION | ALGORITHM | PURSUIT | MATERIALS SCIENCE, CHARACTERIZATION & TESTING
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20. Full Text Multi-Observation Blind Deconvolution with an Adaptive Sparse Prior
by Haichao Zhang and Wipf, David and Yanning Zhang
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 08/2014, Volume 36, Issue 8, pp. 1628 - 1643
This paper describes a robust algorithm for estimating a single latent sharp image given multiple blurry and/or noisy observations. The underlying multi-image... 
Algorithm design and analysis | Deconvolution | Estimation | Cost function | Noise measurement | Kernel | Noise level | sparse estimation | sparse priors | blind image deblurring | Multi-observation blind deconvolution | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Bayesian statistical decision theory | Usage | Image processing | Kernel functions | Innovations | Algorithms | Degradation | Penalty function | Images | Estimating | Joining | Blinds
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