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ORCIDGuy Gilboa
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- affiliation: Technion - Israel Institute of Technology, Haifa, Israel
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2020 – today
- 2024
[j29]Eyal Gofer, Guy Gilboa:
Generalized Inversion of Nonlinear Operators. J. Math. Imaging Vis. 66(4): 478-503 (2024)- [j28]Jonathan Brokman
, Martin Burger, Guy Gilboa:
Spectral Total-variation Processing of Shapes - Theory and Applications. ACM Trans. Graph. 43(2): 22:1-22:20 (2024) - [c38]Jonathan Brokman, Roy Betser, Rotem Turjeman, Tom Berkov, Ido Cohen, Guy Gilboa:
Enhancing Neural Training via a Correlated Dynamics Model. ICLR 2024 - [i30]Elnatan Kadar, Guy Gilboa:
DXAI: Explaining Classification by Image Decomposition. CoRR abs/2401.00320 (2024) - [i29]Meir Yossef Levi, Guy Gilboa:
Fast and Simple Explainability for Point Cloud Networks. CoRR abs/2403.07706 (2024) - 2023
- [c37]Or Streicher, Ido Cohen, Guy Gilboa:
BASiS: Batch Aligned Spectral Embedding Space. CVPR 2023: 10396-10405 - [c36]Meir Yossef Levi, Guy Gilboa:
EPiC: Ensemble of Partial Point Clouds for Robust Classification. ICCV 2023: 14429-14438 - [c35]Eyal Gofer, Guy Gilboa:
Theoretical Foundations for Pseudo-Inversion of Nonlinear Operators. SSVM 2023: 29-41 - [c34]Or Streicher, Guy Gilboa:
Graph Laplacian for Semi-supervised Learning. SSVM 2023: 250-262 - [i28]Or Streicher, Guy Gilboa:
Graph Laplacian for Semi-Supervised Learning. CoRR abs/2301.04956 (2023) - [i27]Meir Yossef Levi, Guy Gilboa:
EPiC: Ensemble of Partial Point Clouds for Robust Classification. CoRR abs/2303.11419 (2023) - [i26]Elnatan Kadar, Jonathan Brokman, Guy Gilboa:
Additive Class Distinction Maps using Branched-GANs. CoRR abs/2305.02899 (2023) - [i25]Chao Wang, Jean-François Aujol, Guy Gilboa, Yifei Lou:
Minimizing Quotient Regularization Model. CoRR abs/2308.04095 (2023) - [i24]Meir Yossef Levi, Guy Gilboa:
Critical Points ++: An Agile Point Cloud Importance Measure for Robust Classification, Adversarial Defense and Explainable AI. CoRR abs/2308.05525 (2023) - [i23]Jonathan Brokman, Roy Betser, Rotem Turjeman, Tom Berkov, Ido Cohen, Guy Gilboa:
Enhancing Neural Training via a Correlated Dynamics Model. CoRR abs/2312.13247 (2023) - 2022
- [j27]Shai Biton, Guy Gilboa:
Adaptive Anisotropic Total Variation: Analysis and Experimental Findings of Nonlinear Spectral Properties. J. Math. Imaging Vis. 64(8): 916-938 (2022) - [i22]Ilya Tcenov, Guy Gilboa:
How to Guide Adaptive Depth Sampling? CoRR abs/2205.10202 (2022) - [i21]Jonathan Brokman, Guy Gilboa:
Analysis of Branch Specialization and its Application in Image Decomposition. CoRR abs/2206.05810 (2022) - [i20]Jonathan Brokman, Martin Burger, Guy Gilboa:
Spectral Total-Variation Processing of Shapes - Theory and Applications. CoRR abs/2209.07517 (2022) - [i19]Or Streicher, Ido Cohen, Guy Gilboa:
BASiS: Batch Aligned Spectral Embedding Space. CoRR abs/2211.16960 (2022) - [i18]Rotem Turjeman, Tom Berkov, Ido Cohen, Guy Gilboa:
The Underlying Correlated Dynamics in Neural Training. CoRR abs/2212.09040 (2022) - 2021
- [j26]Ester Hait-Fraenkel, Guy Gilboa:
Revealing stable and unstable modes of denoisers through nonlinear eigenvalue analysis. J. Vis. Commun. Image Represent. 75: 103041 (2021) - [j25]Ido Cohen, Omri Azencot, Pavel Lifshits, Guy Gilboa:
Modes of Homogeneous Gradient Flows. SIAM J. Imaging Sci. 14(3): 913-945 (2021) - [j24]Leon Bungert, Ester Hait-Fraenkel, Nicolas Papadakis, Guy Gilboa:
Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks. SIAM J. Imaging Sci. 14(3): 1114-1148 (2021) - [j23]Eyal Gofer, Shachar Praisler, Guy Gilboa:
Adaptive LiDAR Sampling and Depth Completion Using Ensemble Variance. IEEE Trans. Image Process. 30: 8900-8912 (2021) - [c33]Damian Kaliroff, Guy Gilboa:
PhIT-Net: Photo-consistent Image Transform for Robust Illumination Invariant Matching. BMVC 2021: 27 - [c32]Jonathan Brokman, Guy Gilboa:
Nonlinear Spectral Processing of Shapes via Zero-Homogeneous Flows. SSVM 2021: 40-51 - [c31]Ido Cohen, Tom Berkov, Guy Gilboa:
Total-Variation Mode Decomposition. SSVM 2021: 52-64 - [i17]Eyal Gofer, Guy Gilboa:
A Pseudo-Inverse for Nonlinear Operators. CoRR abs/2111.10755 (2021) - 2020
- [j22]Ido Cohen, Guy Gilboa:
Introducing the p-Laplacian spectra. Signal Process. 167 (2020) - [c30]Adam Wolff, Shachar Praisler, Ilya Tcenov, Guy Gilboa:
Super-Pixel Sampler: a Data-driven Approach for Depth Sampling and Reconstruction. ICRA 2020: 2588-2594 - [c29]Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola B. Schönlieb:
Deeply Learned Spectral Total Variation Decomposition. NeurIPS 2020 - [i16]Alona Baruhov, Guy Gilboa:
Unsupervised Enhancement of Real-World Depth Images Using Tri-Cycle GAN. CoRR abs/2001.03779 (2020) - [i15]Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola-Bibiane Schönlieb:
Deeply Learned Spectral Total Variation Decomposition. CoRR abs/2006.10004 (2020) - [i14]Ido Cohen, Omri Azencot, Pavel Lifshitz, Guy Gilboa:
Mode Decomposition for Homogeneous Symmetric Operators. CoRR abs/2007.01534 (2020) - [i13]Eyal Gofer, Shachar Praisler, Guy Gilboa:
Adaptive LiDAR Sampling and Depth Completion using Ensemble Variance. CoRR abs/2007.13834 (2020) - [i12]Guy Gilboa:
Iterative Methods for Computing Eigenvectors of Nonlinear Operators. CoRR abs/2010.02890 (2020) - [i11]Eyal Gofer, Guy Gilboa:
Experts with Lower-Bounded Loss Feedback: A Unifying Framework. CoRR abs/2012.09537 (2020)
2010 – 2019
- 2019
- [j21]Ester Hait, Guy Gilboa:
Spectral Total-Variation Local Scale Signatures for Image Manipulation and Fusion. IEEE Trans. Image Process. 28(2): 880-895 (2019) - [c28]Ido Cohen, Adi Falik, Guy Gilboa:
Stable Explicit p-Laplacian Flows Based on Nonlinear Eigenvalue Analysis. SSVM 2019: 315-327 - [i10]Adam Wolff, Shachar Praisler, Ilya Tcenov, Guy Gilboa:
Image-Guided Depth Sampling and Reconstruction. CoRR abs/1908.01379 (2019) - [i9]Ester Hait-Fraenkel, Guy Gilboa:
Numeric Solutions of Eigenvalue Problems for Generic Nonlinear Operators. CoRR abs/1909.12775 (2019) - [i8]Damian Kaliroff, Guy Gilboa:
Self-Supervised Unconstrained Illumination Invariant Representation. CoRR abs/1911.12641 (2019) - 2018
- [b1]Guy Gilboa:
Nonlinear Eigenproblems in Image Processing and Computer Vision. Advances in Computer Vision and Pattern Recognition, Springer 2018, ISBN 978-3-319-75846-6, pp. 1-158 - [j20]Ido Cohen, Guy Gilboa:
Energy dissipating flows for solving nonlinear eigenpair problems. J. Comput. Phys. 375: 1138-1158 (2018) - [j19]Martin Welk, Joachim Weickert, Guy Gilboa:
A Discrete Theory and Efficient Algorithms for Forward-and-Backward Diffusion Filtering. J. Math. Imaging Vis. 60(9): 1399-1426 (2018) - [j18]Raz Z. Nossek, Guy Gilboa:
Flows Generating Nonlinear Eigenfunctions. J. Sci. Comput. 75(2): 859-888 (2018) - [j17]Jean-François Aujol, Guy Gilboa, Nicolas Papadakis:
Theoretical Analysis of Flows Estimating Eigenfunctions of One-Homogeneous Functionals. SIAM J. Imaging Sci. 11(2): 1416-1440 (2018) - 2017
- [j16]Guy Gilboa:
Semi-Inner-Products for Convex Functionals and Their Use in Image Decomposition. J. Math. Imaging Vis. 57(1): 26-42 (2017) - [j15]Ester Hait, Guy Gilboa:
Blind Facial Image Quality Enhancement Using Non-Rigid Semantic Patches. IEEE Trans. Image Process. 26(6): 2705-2720 (2017) - [c27]Ori Spier, Tali Treibitz, Guy Gilboa:
In situ target-less calibration of turbid media. ICCP 2017: 137-145 - [c26]Martin Benning, Michael Möller, Raz Z. Nossek, Martin Burger, Daniel Cremers, Guy Gilboa, Carola-Bibiane Schönlieb:
Nonlinear Spectral Image Fusion. SSVM 2017: 41-53 - [c25]Martin Benning, Guy Gilboa, Joana Sarah Grah, Carola-Bibiane Schönlieb:
Learning Filter Functions in Regularisers by Minimising Quotients. SSVM 2017: 511-523 - [i7]Martin Benning, Michael Möller, Raz Z. Nossek, Martin Burger, Daniel Cremers, Guy Gilboa, Carola-Bibiane Schönlieb:
Nonlinear Spectral Image Fusion. CoRR abs/1703.08001 (2017) - 2016
- [j14]Guy Gilboa, Michael Möller, Martin Burger:
Nonlinear Spectral Analysis via One-Homogeneous Functionals: Overview and Future Prospects. J. Math. Imaging Vis. 56(2): 300-319 (2016) - [j13]Martin Burger, Guy Gilboa, Michael Möller, Lina Eckardt, Daniel Cremers:
Spectral Decompositions Using One-Homogeneous Functionals. SIAM J. Imaging Sci. 9(3): 1374-1408 (2016) - [j12]Dikla Horesh, Guy Gilboa:
Separation Surfaces in the Spectral TV Domain for Texture Decomposition. IEEE Trans. Image Process. 25(9): 4260-4270 (2016) - [c24]Gilad Drozdov, Yevgengy Shapiro, Guy Gilboa:
Robust Recovery of Heavily Degraded Depth Measurements. 3DV 2016: 56-65 - [c23]Daniel Rotman, Guy Gilboa:
A Depth Restoration Occlusionless Temporal Dataset. 3DV 2016: 176-184 - [c22]Moti Freiman, Yechiel Lamash, Guy Gilboa, Hannes Nickisch, Sven Prevrhal, Holger Schmitt, Mani Vembar, Liran Goshen:
Automatic coronary lumen segmentation with partial volume modeling improves lesions' hemodynamic significance assessment. Image Processing 2016: 978403 - [i6]Martin Burger, Guy Gilboa, Michael Möller, Lina Eckardt, Daniel Cremers:
Spectral Decompositions using One-Homogeneous Functionals. CoRR abs/1601.02912 (2016) - [i5]Raz Z. Nossek, Guy Gilboa:
Flows Generating Nonlinear Eigenfunctions. CoRR abs/1609.08438 (2016) - [i4]Ester Hait, Guy Gilboa:
Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches. CoRR abs/1609.08475 (2016) - 2015
- [c21]Michael Möller, Julia Diebold, Guy Gilboa, Daniel Cremers:
Learning Nonlinear Spectral Filters for Color Image Reconstruction. ICCV 2015: 289-297 - [c20]Oren Katzir, Guy Gilboa:
A maximal interest-point strategy applied to image enhancement with external priors. ICIP 2015: 1324-1328 - [c19]Guy Gilboa, Eli Appleboim, Emil Saucan, Yehoshua Y. Zeevi:
On the role of non-local Menger curvature in image processing. ICIP 2015: 4337-4341 - [c18]Martin Burger, Lina Eckardt, Guy Gilboa, Michael Möller:
Spectral Representations of One-Homogeneous Functionals. SSVM 2015: 16-27 - [c17]Jean-François Aujol, Guy Gilboa, Nicolas Papadakis:
Fundamentals of Non-Local Total Variation Spectral Theory. SSVM 2015: 66-77 - [c16]Dikla Horesh, Guy Gilboa:
Multiscale Texture Orientation Analysis Using Spectral Total-Variation Decomposition. SSVM 2015: 486-497 - [i3]Guy Gilboa, Michael Möller, Martin Burger:
Nonlinear Spectral Analysis via One-homogeneous Functionals - Overview and Future Prospects. CoRR abs/1510.01077 (2015) - [i2]Guy Gilboa:
Semi-Inner-Products for Convex Functionals and Their Use in Image Decomposition. CoRR abs/1511.04685 (2015) - [i1]Dikla Horesh, Guy Gilboa:
Separation Surfaces in the Spectral TV Domain for Texture Decomposition. CoRR abs/1511.04687 (2015) - 2014
- [j11]Guy Gilboa:
A Total Variation Spectral Framework for Scale and Texture Analysis. SIAM J. Imaging Sci. 7(4): 1937-1961 (2014) - [c15]Guy Gilboa:
Nonlinear band-pass filtering using the TV transform. EUSIPCO 2014: 1696-1700 - 2013
- [c14]Guy Gilboa:
Expert Regularizers for Task Specific Processing. SSVM 2013: 24-35 - [c13]Guy Gilboa:
A Spectral Approach to Total Variation. SSVM 2013: 36-47
2000 – 2009
- 2009
- [c12]Martin Welk, Guy Gilboa, Joachim Weickert:
Theoretical Foundations for Discrete Forward-and-Backward Diffusion Filtering. SSVM 2009: 527-538 - 2008
- [j10]Guy Gilboa, Stanley J. Osher:
Nonlocal Operators with Applications to Image Processing. Multiscale Model. Simul. 7(3): 1005-1028 (2008) - [j9]Guy Gilboa:
Nonlinear Scale Space with Spatially Varying Stopping Time. IEEE Trans. Pattern Anal. Mach. Intell. 30(12): 2175-2187 (2008) - 2007
- [j8]Guy Gilboa, Stanley J. Osher:
Nonlocal Linear Image Regularization and Supervised Segmentation. Multiscale Model. Simul. 6(2): 595-630 (2007) - [c11]Guy Gilboa, Stanley J. Osher:
Nonlocal evolutions for image regularization. Computational Imaging 2007: 64980U - 2006
- [j7]Jean-François Aujol, Guy Gilboa, Tony F. Chan, Stanley J. Osher:
Structure-Texture Image Decomposition - Modeling, Algorithms, and Parameter Selection. Int. J. Comput. Vis. 67(1): 111-136 (2006) - [j6]Jean-François Aujol, Guy Gilboa:
Constrained and SNR-Based Solutions for TV-Hilbert Space Image Denoising. J. Math. Imaging Vis. 26(1-2): 217-237 (2006) - [j5]Guy Gilboa, Nir A. Sochen, Yehoshua Y. Zeevi:
Estimation of optimal PDE-based denoising in the SNR sense. IEEE Trans. Image Process. 15(8): 2269-2280 (2006) - [j4]Guy Gilboa, Nir A. Sochen, Yehoshua Y. Zeevi:
Variational denoising of partly textured images by spatially varying constraints. IEEE Trans. Image Process. 15(8): 2281-2289 (2006) - 2005
- [c10]Guy Gilboa, Nir A. Sochen, Yehoshua Y. Zeevi:
Estimation of the Optimal Variational Parameter via SNR Analysis. Scale-Space 2005: 230-241 - [c9]Martin Burger, Stanley J. Osher, Jinjun Xu, Guy Gilboa:
Nonlinear Inverse Scale Space Methods for Image Restoration. VLSM 2005: 25-36 - [c8]Jean-François Aujol, Guy Gilboa, Tony F. Chan, Stanley J. Osher:
Structure-Texture Decomposition by a TV-Gabor Model. VLSM 2005: 85-96 - 2004
- [j3]Guy Gilboa, Nir A. Sochen, Yehoshua Y. Zeevi:
Image Sharpening by Flows Based on Triple Well Potentials. J. Math. Imaging Vis. 20(1-2): 121-131 (2004) - [j2]Guy Gilboa, Nir A. Sochen, Yehoshua Y. Zeevi:
Image Enhancement and Denoising by Complex Diffusion Processes. IEEE Trans. Pattern Anal. Mach. Intell. 26(8): 1020-1036 (2004) - 2003
- [c7]Guy Gilboa, Yehoshua Y. Zeevi, Nir A. Sochen:
PDE-based denoising of complex scenes using a spatially-varying fidelity term. ICIP (1) 2003: 865-868 - 2002
- [j1]Guy Gilboa, Nir A. Sochen, Yehoshua Y. Zeevi:
Forward-and-backward diffusion processes for adaptive image enhancement and denoising. IEEE Trans. Image Process. 11(7): 689-703 (2002) - [c6]Guy Gilboa, Nir A. Sochen, Yehoshua Y. Zeevi:
Regularized Shock Filters and Complex Diffusion. ECCV (1) 2002: 399-413 - 2001
- [c5]Guy Gilboa, Yehoshua Y. Zeevi, Nir A. Sochen:
Image enhancement segmentation and denoising by time dependent nonlinear diffusion processes. ICIP (3) 2001: 134-137 - [c4]Guy Gilboa, Yehoshua Y. Zeevi, Nir A. Sochen:
Complex Diffusion Processes for Image Filtering. Scale-Space 2001: 299-307 - 2000
- [c3]Nir A. Sochen, Guy Gilboa, Yehoshua Y. Zeevi:
Color Image Enhancement by a Forward-and-Backward Adaptive Beltrami Flow. AFPAC 2000: 319-328 - [c2]Guy Gilboa, Yehoshua Y. Zeevi, Nir A. Sochen:
Signal and image enhancement by a generalized forward-and-backward adaptive diffusion process. EUSIPCO 2000: 1-4 - [c1]Guy Gilboa, Yehoshua Y. Zeevi, Nir A. Sochen:
Anisotropic selective inverse diffusion for signal enhancement in the presence of noise. ICASSP 2000: 221-224
Coauthor Index
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