PySAP: Python Sparse Data Analysis Package for Multidisciplinary Image Processing
arXiv:1910.08465 · doi:10.1016/j.ascom.2020.100402
Abstract
We present the open-source image processing software package PySAP (Python Sparse data Analysis Package) developed for the COmpressed Sensing for Magnetic resonance Imaging and Cosmology (COSMIC) project. This package provides a set of flexible tools that can be applied to a variety of compressed sensing and image reconstruction problems in various research domains. In particular, PySAP offers fast wavelet transforms and a range of integrated optimisation algorithms. In this paper we present the features available in PySAP and provide practical demonstrations on astrophysical and magnetic resonance imaging data.
arXiv commenthttps://python-pysap.readthedocs.io/
References in corpus (11)
- The Cosmic Evolution Survey (COSMOS) -- Overview
- The COSMOS Survey: Hubble Space Telescope / Advanced Camera for Surveys (HST/ACS) Observations and Data Processing
- COSMOS : Hubble Space Telescope Observations
- Generalized Forward-Backward Splitting
- Network Flow Algorithms for Structured Sparsity
- Joint Planck and WMAP CMB Map Reconstruction
- High Resolution Weak Lensing Mass-Mapping Combining Shear and Flexion
- GLIMPSE: Accurate 3D weak lensing reconstructions using sparsity
- Super-resolution method using sparse regularization for point-spread function recovery
- Space variant deconvolution of galaxy survey images
- Sparse Prediction with the -Support Norm
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