Imaging, vision and learning based on optimization and PDEs : IVLOPDE, Bergen, Norway, August 29 -- September 2, 2016 / editors Xue-Cheng Tai, Egil Bae and Marius Lysaker.
Tai, Xue-Cheng, editor.; Bae, Egil, editor.; Lysaker, Marius, editor.; International Conference "Imaging, vision and learning based on optimization and PDEs" (2016 : Bergen, Norway)
2018
TA1637.5
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Title Imaging, vision and learning based on optimization and PDEs : IVLOPDE, Bergen, Norway, August 29 -- September 2, 2016 / editors Xue-Cheng Tai, Egil Bae and Marius Lysaker.
ISBN 9783319912745 (electronic book)
3319912747 (electronic book)
9783319912738
Published Cham, Switzerland : Springer, [2018]
Language English
Description 1 online resource : illustrations
Call Number TA1637.5
Dewey Decimal Classification 621.367028553
Summary This volume presents the peer-reviewed proceedings of the international conference Imaging, Vision and Learning Based on Optimization and PDEs (IVLOPDE), held in Bergen, Norway, in August/September 2016. The contributions cover state-of-the-art research on mathematical techniques for image processing, computer vision and machine learning based on optimization and partial differential equations (PDEs). It has become an established paradigm to formulate problems within image processing and computer vision as PDEs, variational problems or finite dimensional optimization problems. This compact yet expressive framework makes it possible to incorporate a range of desired properties of the solutions and to design algorithms based on well-founded mathematical theory. A growing body of research has also approached more general problems within data analysis and machine learning from the same perspective, and demonstrated the advantages over earlier, more established algorithms. This volume will appeal to all mathematicians and computer scientists interested in novel techniques and analytical results for optimization, variational models and PDEs, together with experimental results on applications ranging from early image formation to high-level image and data analysis.-- Provided by publisher.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (viewed November 30, 2018).
Added Author Tai, Xue-Cheng, editor.
Bae, Egil, editor.
Lysaker, Marius, editor.
Added Meeting Name International Conference "Imaging, vision and learning based on optimization and PDEs" (2016 : Bergen, Norway)
Series Mathematics and visualization.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Intro; Preface; Contents; Part I Image Reconstruction from Incomplete Data; 1 Adaptive Regularization for Image Reconstruction from Subsampled Data; Introduction; Problem Settings and Notations; Adaptive Regularization Approach; ROF-Model and Surrogate Iteration; Hierarchical Spatially Adaptive Algorithm; Numerical Experiments; Reconstruction of Partial Fourier Data; Wavelet Inpainting; Qualitative Relation to Other Spatially Distributed Parameter Methods; Conclusion; References; 2 A Convergent Fixed-Point Proximity Algorithm Accelerated by FISTA for the 0 Sparse Recovery Problem

IntroductionMinimizers of the Proposed Model; Convergence Analysis of the Proposed Algorithms; Sparse Support Pursuit; Recovery on the Sparse Support; Numerical Experiments; Conclusion; References; 3 Sparse-Data Based 3D Surface Reconstructionfor Cartoon and Map; Introduction; Proposed Model; Augmented Lagrangian Method; Solving the Q-Subproblem (3.7); Solving the P-Subproblem (3.8); Solving the C-Subproblem (3.9); Solving the S-Subproblem (3.10); Solving the I-Subproblem (3.11); Solving the E-Subproblem (3.12); Numerical Results; Conclusion; References

Part II Image Enhancement, Restoration and Registration4 Variational Methods for Gamut Mappingin Cinema and Television; Introduction; Related Work; Gamut Reduction Algorithms (GRAs); Gamut Extension Algorithms (GEAs); Reproduction Intent and Evaluation; Subjective Evaluation; Objective Evaluation; Gamut Mapping in RGB Based on Perceptually-Based Color and Contrast Enhancement; GRA-RGB Zamir2014: Gamut Reduction Algorithm on RGB; GEA-RGB Zamir2014: Gamut Extension Algorithm on RGB; Gamut Extension in CIELAB Color Space; GEA-LAB1 Zamir2015: Gamut Extension Algorithm

GEA-LAB2 Zamir2017: Gamut Extension Algorithm Driven by Hue, Saturation and Chroma ConstraintsQualitative Experiments and Results; Methodology; Results; Temporal Consistency Test; Gamut Mapping Using Kernel Based Retinex (KBR) in HSV Color Space; GEA-KBR Zamir2016: Gamut Extension Algorithm; Results of GEA-KBR; Making the GEA-KBR Faster; GRA-KBR: Gamut Reduction Algorithm; Results of GRA-KBR; Conclusion and Future Work; References; 5 Functional Lifting for Variational Problems with Higher-Order Regularization; Introduction and Related Work; Contributions

Lifting for Absolute Laplacian RegularizationNotation and Mathematical Preliminaries; Approximate Relaxation of the Absolute Laplacian; Experimental Results; Non-convex Denoising with Second-Order Regularity; Image Registration Using the Absolute Laplacian; Translation-Only Synthetic Image; Real-World Image Registration; Conclusion and Outlook; References; 6 On the Convex Model of Speckle Reduction; Introduction; A Convex Model for Despeckling; Speckle Noise; Convex Variational Model for Despeckling; Numerical Scheme Using Bermudez-Moreno Algorithm; Generalized Form

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