M
Mathews Jacob
Researcher at University of Iowa
Publications - 241
Citations - 7610
Mathews Jacob is an academic researcher from University of Iowa. The author has contributed to research in topics: Iterative reconstruction & Compressed sensing. The author has an hindex of 32, co-authored 229 publications receiving 5963 citations. Previous affiliations of Mathews Jacob include École Polytechnique & Oregon State University.
Papers
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Design and Validation of a Tool for Neurite Tracing and Analysis in Fluorescence Microscopy Images
TL;DR: The design and validation of a semiautomatic neurite tracing technique for accurate and reproducible segmentation and quantification of neuronal processes are described.
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MoDL: Model-Based Deep Learning Architecture for Inverse Problems
TL;DR: In this article, a convolution neural network (CNN)-based regularization prior is proposed for inverse problems with the arbitrary structure, where the forward model is explicitly accounted for and a smaller network with fewer parameters is sufficient to capture the image information compared to direct inversion.
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Accelerated Dynamic MRI Exploiting Sparsity and Low-Rank Structure: k-t SLR
TL;DR: A novel algorithm to reconstruct dynamic magnetic resonance imaging data from under-sampled k-t space data using the compact representation of the data in the Karhunen Louve transform (KLT) domain to exploit the correlations in the dataset.
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Design of steerable filters for feature detection using canny-like criteria
Mathews Jacob,Michael Unser +1 more
TL;DR: This work proposes a general approach for the design of 2D feature detectors from a class of steerable functions based on the optimization of a Canny-like criterion that yields operators that have a better orientation selectivity than the classical gradient or Hessian-based detectors.
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MoDL: Model Based Deep Learning Architecture for Inverse Problems.
TL;DR: This work introduces a model-based image reconstruction framework with a convolution neural network (CNN)-based regularization prior, and proposes to enforce data-consistency by using numerical optimization blocks, such as conjugate gradients algorithm within the network.