the proceedings contain 42 papers. the topics discussed include: targeted iterative filtering;generalized gradient on vector bundle application to image denoising;expert regularizers for task specific processing;a spe...
ISBN:
(纸本)9783642382666
the proceedings contain 42 papers. the topics discussed include: targeted iterative filtering;generalized gradient on vector bundle application to image denoising;expert regularizers for task specific processing;a spectral approach to total variation;convex generalizations of total variation based on the structure tensor with applications to inverse problems;adaptive second-order total variation: an approach aware of slope discontinuities;variationalmethods for motion deblurring with still background;blind deblurring using a simplified sharpness index;a cascadic alternating Krylov subspace image restoration method;B-SMART: Bregman-based first-order algorithms for non-negative compressed sensing problems;and epigraphical projection for solving least squares anscombe transformed constrained optimization problems.
the proceedings contain 57 papers. the special focus in this conference is on scalespace and variationalmethods in computervision. the topics include: EmNeF: Neural Fields for Embedded variational Problems in&...
ISBN:
(纸本)9783031319747
the proceedings contain 57 papers. the special focus in this conference is on scalespace and variationalmethods in computervision. 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
the Riemannian metamorphosis model introduced and analyzed in [7,12] is taken into account to develop an image extrapolation tool in the space of images. To this end, the variational time discretization for the geodes...
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ISBN:
(纸本)9783319587714;9783319587707
the Riemannian metamorphosis model introduced and analyzed in [7,12] is taken into account to develop an image extrapolation tool in the space of images. To this end, the variational time discretization for the geodesic interpolation proposed in [2] is picked up to define a discrete exponential map. For a given weakly differentiable initial image and a sufficiently small initial image variation it is shown how to compute a discrete geodesic extrapolation path in the space of images. the resulting discrete paths are indeed local minimizers of the corresponding discrete path energy. A spatial Galerkin discretization with cubic splines on coarse meshes for image deformations and piecewise bilinear finite elements on fine meshes for image intensity functions is used to derive a fully practical algorithm. the method is applied to real images and image variations recorded with a digital camera.
We propose a robust variational model for the restoration of images corrupted by blur and the general class of additive white noises. the solution of the non-trivial optimization problem, due to the non-smooth non-con...
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ISBN:
(纸本)9783319587714;9783319587707
We propose a robust variational model for the restoration of images corrupted by blur and the general class of additive white noises. the solution of the non-trivial optimization problem, due to the non-smooth non-convex proposed model, is efficiently obtained by an Alternating Directions Method of Multipliers (ADMM), which in particular reduces the solution to a sequence of convex optimization sub-problems. Numerical results show the potentiality of the proposed model for restoring blurred images corrupted by several kinds of additive white noises.
Fractional calculus is an extension of integer-order differentiation and integration which explains many natural physical processes. New applications of the fractional calculus are in constant development. the current...
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ISBN:
(纸本)9783030223687;9783030223670
Fractional calculus is an extension of integer-order differentiation and integration which explains many natural physical processes. New applications of the fractional calculus are in constant development. the current paper introduces fractional differentiation to feature detection in digital images. the Harris-Laplace feature detector is adapted to use the non-local properties of the fractional derivative to include more information about image pixel perturbations when quantifying features. Using fractional derivatives in the Harris-Laplace detector leads to higher repeatability when detecting features in grayscale images. Applications of this development are suggested.
We propose a variational approach for edge-preserving total variation (TV)-based regularization of Q-ball data from high angular resolution diffusion imaging (HARDI). While total variation is among the most popular re...
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ISBN:
(纸本)9783319587714;9783319587707
We propose a variational approach for edge-preserving total variation (TV)-based regularization of Q-ball data from high angular resolution diffusion imaging (HARDI). While total variation is among the most popular regularizers for variational problems, its application to orientation distribution functions (ODF), as they naturally arise in Q-ball imaging, is not straightforward. We propose to use an extension that specifically takes into account the metric on the underlying orientation space. the key idea is to write the difference quotients in the TV seminorm in terms of the Wasserstein statistical distance from optimal transport. We combine this regularizer with a matchingWasserstein data fidelity term. Using the Kantorovich-Rubinstein duality, the variational model can be formulated as a convex optimization problem that can be solved using a primal-dual algorithm. We demonstrate the effectiveness of the proposed framework on real and synthetic Q-ball data.
We introduce a method for corner estimation based on the affine morphological scalespace (AMSS). Using some explicit known formula about corner evolution across AMSS, proven by Alvarez and Morales in 1997, we define ...
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ISBN:
(纸本)9783319587714;9783319587707
We introduce a method for corner estimation based on the affine morphological scalespace (AMSS). Using some explicit known formula about corner evolution across AMSS, proven by Alvarez and Morales in 1997, we define a morphological cornerness measure based on the expected evolution of an ideal corner across AMSS. We define a new procedure to track the corner motion across AMSS. To evaluate the accuracy of the method we study in details the results for a collection of synthetic corners with angles from 15 to 160 degrees. We also present experiments in real images and we show that the proposed method can also automatically handle the case of multiple junctions.
this book constitutes the refereed proceedings of the 4thinternationalconference on scalespacemethods and variationalmethods in computervision, ssvm 2013, held in Schloss Seggau near Graz, Austria, in June 2013....
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ISBN:
(数字)9783642382673
ISBN:
(纸本)9783642382666
this book constitutes the refereed proceedings of the 4thinternationalconference on scalespacemethods and variationalmethods in computervision, ssvm 2013, held in Schloss Seggau near Graz, Austria, in June 2013.
the 42 revised full papers presented were carefully reviewed and selected 69 submissions. the papers are organized in topical sections on image denoising and restoration, image enhancement and texture synthesis, optical flow and 3D reconstruction, scalespace and partial differential equations, image and shape analysis, and segmentation.
We introduce a method to approximately minimize variational models with Total Generalized Variation regularization (TGV) and non-convex data terms. Our approach is based on a decomposition of the functional into two s...
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