Yashil Sukurdeep
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View article: ImageMM: Joint Multi-frame Image Restoration and Super-resolution
ImageMM: Joint Multi-frame Image Restoration and Super-resolution Open
A key processing step in ground-based astronomy involves combining multiple noisy and blurry exposures to produce an image of the night sky with an improved signal-to-noise ratio. Typically, this is achieved via image coaddition, and can b…
View article: AstroClearNet: Deep image prior for multi-frame astronomical image restoration
AstroClearNet: Deep image prior for multi-frame astronomical image restoration Open
View article: A computational pipeline for clustering left atrial appendage morphology via elastic shape analysis.
A computational pipeline for clustering left atrial appendage morphology via elastic shape analysis. Open
Morphological variations in the left atrial appendage (LAA) are associated with different levels of ischemic stroke risk for patients with atrial fibrillation (AF). Studying LAA morphology can elucidate mechanisms behind this association a…
View article: ImageMM: Joint multi-frame image restoration and super-resolution
ImageMM: Joint multi-frame image restoration and super-resolution Open
A key processing step in ground-based astronomy involves combining multiple noisy and blurry exposures to produce an image of the night sky with an improved signal-to-noise ratio. Typically, this is achieved via image coaddition, and can b…
View article: Astroclearnet: Deep Image Prior for Multi-Frame Astronomical Image Restoration
Astroclearnet: Deep Image Prior for Multi-Frame Astronomical Image Restoration Open
View article: A flexible Expectation-Maximization framework for fast, scalable and high-fidelity multi-frame astronomical image deconvolution
A flexible Expectation-Maximization framework for fast, scalable and high-fidelity multi-frame astronomical image deconvolution Open
We present a computationally efficient expectation-maximization framework for multi-frame image deconvolution and super-resolution. Our method is well adapted for processing large scale imaging data from modern astronomical surveys. Our Te…
View article: Learning the Night Sky with Deep Generative Priors
Learning the Night Sky with Deep Generative Priors Open
Recovering sharper images from blurred observations, referred to as deconvolution, is an ill-posed problem where classical approaches often produce unsatisfactory results. In ground-based astronomy, combining multiple exposures to achieve …
View article: Elastic Shape Analysis of Surfaces with Second-Order Sobolev Metrics: A Comprehensive Numerical Framework
Elastic Shape Analysis of Surfaces with Second-Order Sobolev Metrics: A Comprehensive Numerical Framework Open
This paper introduces a set of numerical methods for Riemannian shape analysis of 3D surfaces within the setting of invariant (elastic) second-order Sobolev metrics. More specifically, we address the computation of geodesics and geodesic d…
View article: Elastic shape analysis of surfaces with second-order Sobolev metrics: a comprehensive numerical framework
Elastic shape analysis of surfaces with second-order Sobolev metrics: a comprehensive numerical framework Open
This paper introduces a set of numerical methods for Riemannian shape analysis of 3D surfaces within the setting of invariant (elastic) second-order Sobolev metrics. More specifically, we address the computation of geodesics and geodesic d…
View article: A New Variational Model for Shape Graph Registration with Partial Matching Constraints
A New Variational Model for Shape Graph Registration with Partial Matching Constraints Open
This paper introduces a new extension of Riemannian elastic curve matching to\na general class of geometric structures, which we call (weighted) shape graphs,\nthat allows for shape registration with partial matching constraints and\ntopol…
View article: Supervised deep learning of elastic SRV distances on the shape space of curves
Supervised deep learning of elastic SRV distances on the shape space of curves Open
Motivated by applications from computer vision to bioinformatics, the field of shape analysis deals with problems where one wants to analyze geometric objects, such as curves, while ignoring actions that preserve their shape, such as trans…
View article: A new variational model for the analysis of shape graphs with partial matching constraints
A new variational model for the analysis of shape graphs with partial matching constraints Open
This paper introduces a new extension of Riemannian elastic curve matching to a general class of geometric structures, which we call (weighted) shape graphs, that allows for shape registration with partial matching constraints and topologi…
View article: A new variational model for shape graph registration with partial matching constraints
A new variational model for shape graph registration with partial matching constraints Open
This paper introduces a new extension of Riemannian elastic curve matching to a general class of geometric structures, which we call (weighted) shape graphs, that allows for shape registration with partial matching constraints and topologi…
View article: Supervised deep learning of elastic SRV distances on the shape space of\n curves
Supervised deep learning of elastic SRV distances on the shape space of\n curves Open
Motivated by applications from computer vision to bioinformatics, the field\nof shape analysis deals with problems where one wants to analyze geometric\nobjects, such as curves, while ignoring actions that preserve their shape, such\nas tr…
View article: An inexact matching approach for the comparison of plane curves with general elastic metrics
An inexact matching approach for the comparison of plane curves with general elastic metrics Open
This paper introduces a new mathematical formulation and numerical approach\nfor the computation of distances and geodesics between immersed planar curves.\nOur approach combines the general simplifying transform for first-order elastic\nm…