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检索条件"任意字段=9th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2023"

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9th international conference on scale space and variational methods in computer vision, ssvm 2023

9th International Conference on Scale Space and Variational ...

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9th international conference on scale space and variational methods in computer vision, ssvm 2023

ISBN: (纸本)9783031319747

the proceedings contain 57 papers. the special focus in this conference is on scale space and variational methods in computer vision. the topics include: EmNeF: Neural Fields for Embedded variational Problems in Imaging;genHarris-ResNet: A Rotation Invariant Neural Network Based on Elementary Symmetric Polynomials;compressive Learning of Deep Regularization for Denoising;graph Laplacian and Neural Networks for Inverse Problems in Imaging: GraphLaNet;learning Posterior Distributions in Underdetermined Inverse Problems;proximal Residual Flows for Bayesian Inverse Problems;a Model is Worth Tens of thousands of Examples;resolution-Invariant Image Classification Based on Fourier Neural Operators;graph Laplacian for Semi-supervised Learning;efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting;a Geometrically Aware Auto-Encoder for Multi-texture Synthesis;Fast Marching Energy CNN;deep Accurate Solver for the Geodesic Problem;deep Image Prior Regularized by Coupled Total Variation for Image Colorization;hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras;latent-space Disentanglement with Untrained Generator Networks for the Isolation of Different Motion Types in Video Data;natural Numerical Networks on Directed Graphs in Satellite Image Classification;piece-wise Constant Image Segmentation with a Deep Image Prior Approach;On the Inclusion of Topological Requirements in CNNs for Semantic Segmentation Applied to Radiotherapy;a Relaxed Proximal Gradient Descent Algorithm for Convergent Plug-and-Play with Proximal Denoiser;theoretical Foundations for Pseudo-Inversion of Nonlinear Operators;off-the-Grid Charge Algorithm for Curve Reconstruction in Inverse Problems;convergence Guarantees of Overparametrized Wide Deep Inverse Prior;On the Remarkable Efficiency of SMART;wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line;a Quasi-Newton Primal-Dual Algorithm with Line Search;stochastic Gradient D

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EmNeF: Neural Fields for Embedded variational Problems in Imaging 9th

EmNeF: Neural Fields for Embedded Variational Problems in ...

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9th international conference on scale space and variational methods in computer vision, ssvm 2023

作者： Bednarski, Danielle Lellmann, Jan Institute of Mathematics and Image Computing University of Lübeck Lübeck Germany

ISBN: (纸本)9783031319747

We propose a model-driven neural fields approach for solving variational problems. the approach can be applied to a variety of problems with convex, 1-homogeneous regularizer and arbitrary, possibly non-convex, data term. Our strategy is to embed the non-convex energy into a higher-dimensional space, reaching a convex primal-dual formulation. Instead of using classical gradient-descent based optimization algorithms, we propose training multiple fields representing the primal and dual variables in order to solve the problem. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Gradient methods

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scale space and variational methods in computer vision 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Luca Calatroni Marco Donatelli Serena Morigi Marco Prato Matteo Santacesaria

ISBN: (数字)9783031319754

ISBN: (纸本)9783031319747

this book constitutes the proceedings of the 9th international conference on scale space and variational methods in computer vision, ssvm 2023, which took place in Santa Margherita di Pula, Italy, in May 2023.;the 57 papers presented in this volume were carefully reviewed and selected from 72 submissions. they were organized in topical sections as follows: Inverse Problems in Imaging; Machine and Deep Learning in Imaging; Optimization for Imaging: theory and methods; scale space, PDEs, Flow, Motion and Registration.

关键词： Image Processing and computer vision computer Communication Networks computer Applications Machine Learning Mathematics of Computing Pattern Recognition

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Alternate Structural-Textural Video Inpainting for Spot Defects Correction in Movies 7th

Alternate Structural-Textural Video Inpainting for Spot Defe...

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7th international conference on scale space and variational methods in computer vision (ssvm)

作者： Renaudeau, Arthur Lauze, Francois Pierre, Fabien Aujol, Jean-Francois Durou, Jean-Denis Univ Toulouse UMR CNRS 5505 IRIT Toulouse France Univ Copenhagen DIKU Copenhagen Denmark Univ Lorraine UMR CNRS 7503 LORIA Inria Projet Magrit Nancy France Univ Bordeaux Bordeaux INP CNRS IMBUMR 5251 F-33400 Talence France

ISBN: (纸本)9783030223687;9783030223670

We propose a new video inpainting model for movies restoration application. Our model combines structural reconstruction with a diffusion-based method and textural reconstruction with a patch-based method. Both proposed energies (one for each method) are alternatively minimized in order to preserve the overall structure while adding textural refinement. While the structural reconstruction is obtained jointly with optical flow computation with several proximal approaches, the textural reconstruction is processed by a variational non-local approach. Preliminary results on different Middlebury frames show quality improvement in the reconstruction.

关键词： computer vision

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Blind space-Variant Single-Image Restoration of Defocus Blur 6th

Blind Space-Variant Single-Image Restoration of Defocus Blur

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6th international conference on scale space and variational methods in computer vision (ssvm)

作者： Bar, Leah Sochen, Nir Kiryati, Nahum Tel Aviv Univ Dept Appl Math IL-69978 Tel Aviv Israel Tel Aviv Univ Sch Elect Engn IL-69978 Tel Aviv Israel

ISBN: (纸本)9783319587714;9783319587707

We address the problem of blind piecewise space-variant image deblurring where only part of the image is sharp, assuming a shallow depth of field which imposes significant defocus blur. We propose an automatic image recovery approach which segments the sharp and blurred sub-regions, iteratively estimates a non-parametric blur kernel and restores the sharp image via a variational non-blind space variant method. We present a simple and efficient blur measure which emphasizes the blur difference of the sub-regions followed by a blur segmentation procedure based on an evolving level set function. One of the contributions of this work is the extension to the space-variant case of progressive blind deconvolution recently proposed, an iterative process consisting of non-parametric blind kernel estimation and residual blur deblurring. Apparently this progressive strategy is superior to the one step deconvolution procedure. Experimental results on real images demonstrate the effectiveness of the proposed algorithm.

关键词： space-variant deblurring Blind deconvolution Blur segmentation

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Generalised scale-space Properties for Probabilistic Diffusion Models 9th

Generalised Scale-Space Properties for Probabilistic Diffus...

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9th international conference on scale space and variational methods in computer vision, ssvm 2023

作者： Peter, Pascal Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Campus E1.7 Saarland University Saarbrücken66041 Germany

ISBN: (纸本)9783031319747

Probabilistic diffusion models enjoy increasing popularity in the deep learning community. they generate convincing samples from a learned distribution of input images with a wide field of practical applications. Originally, these approaches were motivated from drift-diffusion processes, but these origins find less attention in recent, practice-oriented publications. We investigate probabilistic diffusion models from the viewpoint of scale-space research and show that they fulfil generalised scale-space properties on evolving probability distributions. Moreover, we discuss similarities and differences between interpretations of the physical core concept of drift-diffusion in the deep learning and model-based world. To this end, we examine relations of probabilistic diffusion to osmosis filters. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Probability distributions

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Learning Differential Invariants of Planar Curves 9th

Learning Differential Invariants of Planar Curves

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9th international conference on scale space and variational methods in computer vision, ssvm 2023

作者： Velich, Roy Kimmel, Ron Technion - Israel Institute of Technology Haifa Israel

ISBN: (纸本)9783031319747

We propose a learning paradigm for the numerical approximation of differential invariants of planar curves. Deep neural-networks’ (DNNs) universal approximation properties are utilized to estimate geometric measures. the proposed framework is shown to be a preferable alternative to axiomatic constructions. Specifically, we show that DNNs can learn to overcome instabilities and sampling artifacts and produce consistent signatures for curves subject to a given group of transformations in the plane. We compare the proposed schemes to alternative state-of-the-art axiomatic constructions of differential invariants. We evaluate our models qualitatively and quantitatively and propose a benchmark dataset to evaluate approximation models of differential invariants of planar curves. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer vision

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Learned Discretization Schemes for the Second-Order Total Generalized Variation 9th

Learned Discretization Schemes for the Second-Order Total ...

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9th international conference on scale space and variational methods in computer vision, ssvm 2023

作者： Bogensperger, Lea Chambolle, Antonin Effland, Alexander Pock, thomas Institute of Computer Graphics and Vision Graz University of Technology Graz Austria CEREMADE CNRS & Université Paris-Dauphine PSL Paris France Institute for Applied Mathematics University of Bonn Bonn Germany

ISBN: (纸本)9783031319747

the total generalized variation extends the total variation by incorporating higher-order smoothness. thus, it can also suffer from similar discretization issues related to isotropy. Inspired by the success of novel discretization schemes of the total variation, there has been recent work to improve the second-order total generalized variation discretization, based on the same design idea. In this work, we propose to extend this to a general discretization scheme based on interpolation filters, for which we prove variational consistency. We then describe how to learn these interpolation filters to optimize the discretization for various imaging applications. We illustrate the performance of the method on a synthetic data set as well as for natural image denoising. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image denoising

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Latent-space Disentanglement with Untrained Generator Networks for the Isolation of Different Motion Types in Video Data 9th

Latent-Space Disentanglement with Untrained Generator Netwo...

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9th international conference on scale space and variational methods in computer vision, ssvm 2023

作者： Abdullah, Abdullah Holler, Martin Kunisch, Karl Landman, Malena Sabate Department of Mathematics The Chinese University of Hong Kong Hong Kong Institute of Mathematics and Scientific Computing University of Graz Graz Austria Department of Mathematics Emory University Atlanta United States

ISBN: (纸本)9783031319747

Isolating different types of motion in video data is a highly relevant problem in video analysis. Applications can be found, for example, in dynamic medical or biological imaging, where the analysis and further processing of the dynamics of interest is often complicated by additional, unwanted dynamics, such as motion of the measurement subject. In this work, it is empirically shown that a representation of video data via untrained generator networks, together with a specific technique for latent space disentanglement that uses minimal, one-dimensional information on some of the underlying dynamics, allows to efficiently isolate different, highly non-linear motion types. In particular, such a representation allows to freeze any selection of motion types, and to obtain accurate independent representations of other dynamics of interest. Obtaining such a representation does not require any pre-training on a training data set, i.e., all parameters of the generator network are learned directly from a single video. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Regularized Material Decomposition for K-edge Separation in Hyperspectral Computed Tomography 9th

Regularized Material Decomposition for K-edge Separation in...

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9th international conference on scale space and variational methods in computer vision, ssvm 2023

作者： Bevilacqua, Francesca Dong, Yiqiu Jørgensen, Jakob Sauer Department of Mathematics University of Bologna Bologna Italy Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark

ISBN: (纸本)9783031319747

Hyperspectral computed tomography is a developing technique that exploits the property of materials to attenuate X-rays in different quantities depending on the specific energy. It allows to not only reconstruct the object, but also to estimate the concentration of the materials which compose it. the objective of the present study is to obtain an accurate material decomposition from noisy few-projection data. A preliminary comparative study of reconstruction methods based on material decomposition is performed, employing a phantom composed of materials with similar attenuation profiles with characteristic K-edges separated by only 2, 4 and 6 keV. It is found that a one-stage method encompassing both material decomposition and tomographic reconstruction in a single variational model performs better than a more conventional two-stage approach. It is further found that better modelling of noise through use of a weighted least-squares data fidelity improves reconstruction and material separation, as does the use total variation and L1-norm regularization. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computerized tomography

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