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Deconvolution for Large Astronomical Surveys using the Scaled Gradient Projection method

The code is tested majorly when opt.use_sextractor and opt.use_subdiv are used to run the code.

This repository contains code for image deconvolution using the Scaled Gradient Projection method (SGP) and has been designed for applications in astronomy. The code currently can perform single-image deconvolution with a known Point Spread Function.

Motivation

Ground-based astronomical observations are degraded by several factors such as atmospheric seeing, instrumental aberrations, diffraction, and other sources of noise. Deconvolution can help reverse these effects and extract more science from astronomical images than we currently can.

Scientific details

Coming soon...

Usage

  • run.py contains the driver code.
  • sgp.py contains the SGP algorithm (see the function sgp). There is also a function named sgp_betaDiv. It is the SGP algorithm with beta divergence (see this paper), but is not used in this work.
  • Afunction.py describes procedures for convolution with PSF.
  • flux_conserve_proj.py contains the implementation of the projection step in SGP.
  • utils.py contains some utility functions and constants.py defines some constants.
  • richardson_lucy.py contains the Richardson-Lucy algorithm implementation.
  • ZTF_Deconvolution_Run.ipynb demonstrates an example to use the SGP algorithm for deconvolving ZTF images.
  • ZTF_Deconvolution_Analysis.ipynb illustrates the analysis of deconvolution performance (analysis of crossmatched catalogs, visualizations, etc.) and also code for generating plots in the paper.
  • The folder sgp_reconstruction_results contains SExtractor parameter and configuration files. This is also the folder where the outputs will be stored after deconvolution is run.

License

MIT

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Image deconvolution for ZTF

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