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共 291 条记录，以下是71-80 订阅

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Multi-frame Super-Resolution from Noisy Data 8th

Multi-frame Super-Resolution from Noisy Data

引用

8th international conference on scale space and variational methods in computer vision, SSVM 2021

作者： Bodduna, Kireeti Weickert, Joachim Cárdenas, Marcelo Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University Saarbrücken66041 Germany

ISBN: (纸本)9783030755485

Obtaining high resolution images from low resolution data with clipped noise is algorithmically challenging due to the ill-posed nature of the problem. So far such problems have hardly been tackled, and the few existing approaches use simplistic regularisers. We show the usefulness of two adaptive regularisers based on anisotropic diffusion ideas: Apart from evaluating the classical edge-enhancing anisotropic diffusion regulariser, we introduce a novel non-local one with one-sided differences and superior performance. It is termed sector diffusion. We combine it with all six variants of the classical super-resolution observational model that arise from permutations of its three operators for warping, blurring, and downsampling. Surprisingly, the evaluation in a practically relevant noisy scenario produces a different ranking than the one in the noise-free setting in our previous work (SSVM 2017). © 2021, Springer Nature Switzerland AG.

关键词： Diffusion

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A scaled and Adaptive FISTA Algorithm for Signal-Dependent Sparse Image Super-Resolution Problems 8th

A Scaled and Adaptive FISTA Algorithm for Signal-Dependent S...

引用

8th international conference on scale space and variational methods in computer vision, SSVM 2021

作者： Lazzaretti, Marta Rebegoldi, Simone Calatroni, Luca Estatico, Claudio Dip. di Matematica Università di Genova Via Dodecaneso 35 Genoa16146 Italy Dip. di Ingegneria Industriale Università di Firenze Via di S. Marta 3 Florence50139 Italy CNRS UCA Inria Lab. I3S 2000 Route des Lucioles Sophia-Antipolis06903 France

ISBN: (纸本)9783030755485

We propose a scaled adaptive version of the Fast Iterative Soft-Thresholding Algorithm, named S-FISTA, for the efficient solution of convex optimization problems with sparsity-enforcing regularization. S-FISTA couples a non-monotone backtracking procedure with a scaling strategy for the proximal–gradient step, which is particularly effective in situations where signal-dependent noise is present in the data. The proposed algorithm is tested on some image super-resolution problems where a sparsity-promoting regularization term is coupled with a weighted- 2 data fidelity. Our numerical experiments show that S-FISTA allows for faster convergence in function values with respect to standard FISTA, as well as being an efficient inner solver for iteratively reweighted 1 algorithms, thus reducing the overall computational times. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders

DECA: Deep viewpoint-Equivariant human pose estimation using...

引用

international conference on computer vision (ICCV)

作者： Nicola Garau Niccolò Bisagno Piotr Bródka Nicola Conci University of Trento Povo Trento TN

ISBN: (纸本)9781665428132

Human Pose Estimation (HPE) aims at retrieving the 3D position of human joints from images or videos. We show that current 3D HPE methods suffer a lack of viewpoint equivariance, namely they tend to fail or perform poorly when dealing with viewpoints unseen at training time. Deep learning methods often rely on either scale-invariant, translation-invariant, or rotation-invariant operations, such as max-pooling. However, the adoption of such procedures does not necessarily improve viewpoint generalization, rather leading to more data-dependent methods. To tackle this issue, we propose a novel capsule autoencoder network with fast variational Bayes capsule routing, named DECA. By modeling each joint as a capsule entity, combined with the routing algorithm, our approach can preserve the joints’ hierarchical and geometrical structure in the feature space, independently from the viewpoint. By achieving viewpoint equivariance, we drastically reduce the network data dependency at training time, resulting in an improved ability to generalize for unseen viewpoints. In the experimental validation, we outperform other methods on depth images from both seen and unseen viewpoints, both top-view, and front-view. In the RGB domain, the same network gives state-of-the-art results on the challenging viewpoint transfer task, also establishing a new framework for top-view HPE. The code can be found at https://***/mmlab-cv/DECA.

关键词： Training Deep learning computer vision Three-dimensional displays Codes Pose estimation Routing

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Total Directional Variation for Video Denoising 1

引用

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

作者： Parisotto, Simone Schonlieb, Carola-Bibiane Univ Cambridge CCA Wilberforce Rd Cambridge CB3 0WA England Univ Cambridge DAMTP Wilberforce Rd Cambridge CB3 0WA England

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

In this paper we propose a variational approach for video denoising, based on a total directional variation (TDV) regulariser proposed in [20,21] for image denoising and interpolation. In the TDV regulariser, the underlying image structure is encoded by means of weighted derivatives so as to enhance the anisotropic structures in images, e.g. stripes or curves with a dominant local directionality. For the extension of TDV to video denoising, the space-time structure is captured by the volumetric structure tensor guiding the smoothing process. We discuss this and present our whole video denoising workflow. The numerical results are compared with some state-of-the-art video denoising methods.

关键词： Total directional variation Video denoising Anisotropy Structure tensor variational methods

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Functional Liftings of Vectorial variational Problems with Laplacian Regularization 1

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

作者： Vogt, Thomas Lellmann, Jan Univ Lubeck Inst Math & Image Comp MIC Maria Goeppert Str 3 D-23562 Lubeck Germany

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

We propose a functional lifting-based convex relaxation of variational problems with Laplacian-based second-order regularization. The approach rests on ideas from the calibration method as well as from sublabel-accurate continuous multilabeling approaches, and makes these approaches amenable for variational problems with vectorial data and higher-order regularization, as is common in image processing applications. We motivate the approach in the function space setting and prove that, in the special case of absolute Laplacian regularization, it encompasses the discretization-first sublabel-accurate continuous multilabeling approach as a special case. We present a mathematical connection between the lifted and original functional and discuss possible interpretations of minimizers in the lifted function space. Finally, we exemplarily apply the proposed approach to 2D image registration problems.

关键词： variational methods Curvature regularization Convex relaxation Functional lifting Measure-based regularization

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variational Image Registration for Inhomogeneous-Resolution Pairs 1

引用

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

作者： Hosoya, Kento Imiya, Atsushi Chiba Univ Sch Sci & Engn Inage Ku Yayoi Cho 1-33 Chiba 2638522 Japan Chiba Univ Inst Management & Informat Technol Inage Ku Yayoi Cho 1-33 Chiba 2638522 Japan

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

We propose a variational image registration method for a pair of images with different resolutions. Traditional image registration methods match images assuming that the resolutions of the reference and target images are homogeneous. For the registration of inhomogeneous-resolution image pairs, we first introduce a resolution-conversion method to harmonise the resolution of a pair of images using the rational-order pyramid ***, we develop a variational method for image registration using this resolution-conversion method.

关键词： Image registration

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Joint CNN and variational Model for Fully-Automatic Image Colorization 1

引用

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

作者： Mouzon, Thomas Pierre, Fabien Berger, Marie-Odile Univ Lorraine Lab Lorrain Rech Informat & Ses Applicat CNRS INRIA Projet MagritUMR 7503 Vandoeuvre Les Nancy France

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

This paper aims to couple the powerful prediction of the convolutional neural network (CNN) to the accuracy at pixel scale of the variational methods. In this work, the limitations of the CNN-based image colorization approaches are described. We then focus on a CNN which is able to compute a statistical distribution of the colors for each pixel of the image based on a learning stage on a large color image database. After describing its limitation, the variational method of [17] is briefly recalled. This method is able to select a color candidate among a given set while performing regularization of the result. By combining this approach with a CNN, we designed a fully automatic image colorization framework with an improved accuracy in comparison with CNN alone. Some numerical experiments demonstrate the increased accuracy reached by our method.

关键词： Colorization Convolutional Neural Networks Total variation Optimization

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Aorta Centerline Smoothing and Registration Using variational Models 1

引用

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

作者： Alvarez, Luis Santana-Cedres, Daniel Tahoces, Pablo G. Carreira, Jose M. Univ Las Palmas Gran Canaria CTIM DIS Las Palmas Gran Canaria Spain Univ Santiago de Compostela DEC Santiago De Compostela Spain Complejo Hosp Univ Santiago CHUS Santiago De Compostela Spain

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

In this work we present an application of variational techniques to the smoothing and registration of aorta centerlines. We assume that a 3D segmentation of the aorta lumen and an initial estimation of the aorta centerline are available. The centerline smoothing technique aims to maximize the distance of the centerline to the boundary of the aorta lumen segmentation but keeping the curve smooth. The proposed registration technique computes a rigid transformation by minimizing the squared Euclidean distance between the points of the curves, using landmarks and taking into account that the curves can be of different lengths. We present a variety of experiments on synthetic and real scenarios in order to show the performance of the methods.

关键词： Aorta centerline 3D curve smoothing 3D curve registration variational methods

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Lattice Metric space Application to Grain Defect Detection 1

引用

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

作者： He, Yuchen Kang, Sung Ha Georgia Inst Technol Sch Math Atlanta GA 30332 USA

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

We propose a new model for grain defect detection based on the theory of lattice metric space [7]. The lattice metric space (L, d(L)) shows outstanding advantages in representing lattices. Utilizing this advantage, we propose a new algorithm, Lattice clustering algorithm (LCA). After over-segmentation using regularized k-means, the merging stage is built upon the lattice equivalence relation. Since LCA is built upon (L, d(L)), it is robust against missing particles, deficient hexagonal cells, and can handle non-hexagonal lattices without any modification. We present various numerical experiments to validate our method and investigate interesting properties.

关键词： Defects

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Provably scale-Covariant Networks from Oriented Quasi Quadrature Measures in Cascade 1

引用

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

作者： Lindeberg, Tony KTH Royal Inst Technol Div Computat Sci & Technol Computat Brain Sci Lab Stockholm Sweden

ISBN: (数字)9783030223687

ISBN: (纸本)9783030223687;9783030223670

This article presents a continuous model for hierarchical networks based on a combination of mathematically derived models of receptive fields and biologically inspired computations. Based on a functional model of complex cells in terms of an oriented quasi quadrature combination of first- and second-order directional Gaussian derivatives, we couple such primitive computations in cascade over combinatorial expansions over image orientations. scale-space properties of the computational primitives are analysed and it is shown that the resulting representation allows for provable scale and rotation covariance. A prototype application to texture analysis is developed and it is demonstrated that a simplified mean-reduced representation of the resulting QuasiQuadNet leads to promising experimental results on three texture datasets.

关键词： Textures

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