CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping [0.03%]
基于深层展开的运动矫正定量R2*映射框架（CoRRECT）
Xiaojian Xu,Weijie Gan,Satya V V N Kothapalli et al.
Xiaojian Xu et al.
Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters. Traditional qMRI methods usually deal separately with artifacts arising from accelerated data acquisition, in...
Antonin Chambolle,Claire Delplancke,Matthias J Ehrhardt et al.
Antonin Chambolle et al.
In this work, we propose a new primal-dual algorithm with adaptive step sizes. The stochastic primal-dual hybrid gradient (SPDHG) algorithm with constant step sizes has become widely applied in large-scale convex optimization across many sc...
Elisa Davoli,Rita Ferreira,Irene Fonseca et al.
Elisa Davoli et al.
Due to their ability to handle discontinuous images while having a well-understood behavior, regularizations with total variation (TV) and total generalized variation (TGV) are some of the best-known methods in image denoising. However, lik...
Kristina Schaefer,Joachim Weickert
Kristina Schaefer
We introduce regularised diffusion-shock (RDS) inpainting as a modification of diffusion-shock inpainting from our SSVM 2023 conference paper. RDS inpainting combines two carefully chosen components: homogeneous diffusion and coherence-enha...
Fabian Parzer,Clemens Kirisits,Otmar Scherzer
Fabian Parzer
We consider the problem of blob detection for uncertain images, such as images that have to be inferred from noisy measurements. Extending recent work motivated by astronomical applications, we propose an approach that represents the uncert...
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...