Tobias Alt,Karl Schrader,Matthias Augustin et al.
Tobias Alt et al.
We investigate numerous structural connections between numerical algorithms for partial differential equations (PDEs) and neural architectures. Our goal is to transfer the rich set of mathematical foundations from the world of PDEs to neura...
Image Reconstruction in Light-Sheet Microscopy: Spatially Varying Deconvolution and Mixed Noise [0.03%]
轻片显微镜中的图像重建：空间变化的反卷积和混合噪声
Bogdan Toader,Jérôme Boulanger,Yury Korolev et al.
Bogdan Toader et al.
We study the problem of deconvolution for light-sheet microscopy, where the data is corrupted by spatially varying blur and a combination of Poisson and Gaussian noise. The spatial variation of the point spread function of a light-sheet mic...
Radon Cumulative Distribution Transform Subspace Modeling for Image Classification [0.03%]
基于 Radon 累积分布变换的子空间模型图像分类方法
Mohammad Shifat-E-Rabbi,Xuwang Yin,Abu Hasnat Mohammad Rubaiyat et al.
Mohammad Shifat-E-Rabbi et al.
We present a new supervised image classification method applicable to a broad class of image deformation models. The method makes use of the previously described Radon Cumulative Distribution Transform (R-CDT) for image data, whose mathemat...
Robust PCA via Regularized Reaper with a Matrix-Free Proximal Algorithm [0.03%]
通过矩阵自由邻近算法的正则化Reaper实现鲁棒PCA
Robert Beinert,Gabriele Steidl
Robert Beinert
Principal component analysis (PCA) is known to be sensitive to outliers, so that various robust PCA variants were proposed in the literature. A recent model, called reaper, aims to find the principal components by solving a convex optimizat...
Accelerated Variational PDEs for Efficient Solution of Regularized Inversion Problems [0.03%]
求解正则化反问题的加速变分PDE方法
Minas Benyamin,Jeff Calder,Ganesh Sundaramoorthi et al.
Minas Benyamin et al.
We further develop a new framework, called PDE acceleration, by applying it to calculus of variation problems defined for general functions on ℝ n , obtaining efficient numerical algorithms to solve the resulting class of optimizat...
Alexander Effland,Erich Kobler,Thomas Pock et al.
Alexander Effland et al.
This paper combines image metamorphosis with deep features. To this end, images are considered as maps into a high-dimensional feature space and a structure-sensitive, anisotropic flow regularization is incorporated in the metamorphosis mod...
Sparse reconstruction of log-conductivity in current density impedance tomography [0.03%]
电流密度阻抗断层扫描中日志电导率的稀疏重建
Madhu Gupta,Rohit Kumar Mishra,Souvik Roy
Madhu Gupta
A new non-linear optimization approach is proposed for the sparse reconstruction of log-conductivities in current density impedance imaging. This framework comprises of minimizing an objective functional involving a least squares fit of the...
Bilevel Parameter Learning for Higher-Order Total Variation Regularisation Models [0.03%]
高阶总变异regularisation模型的 bilevel 参数学习法
J C De Los Reyes,C-B Schönlieb,T Valkonen
J C De Los Reyes
We consider a bilevel optimisation approach for parameter learning in higher-order total variation image reconstruction models. Apart from the least squares cost functional, naturally used in bilevel learning, we propose and analyse an alte...
Johannes Schwab,Stephan Antholzer,Markus Haltmeier
Johannes Schwab
Deep learning and (deep) neural networks are emerging tools to address inverse problems and image reconstruction tasks. Despite outstanding performance, the mathematical analysis for solving inverse problems by neural networks is mostly mis...
S Arridge,A Hauptmann
S Arridge
A multitude of imaging and vision tasks have seen recently a major transformation by deep learning methods and in particular by the application of convolutional neural networks. These methods achieve impressive results, even for application...