This repository contains the code implementation accompanying the paper: $\beta$-SGP: Scaled Gradient Projection with $\beta$-divergence for astronomical image restoration. It is aimed at single-image deconvolution of astronomical images with a known Point Spread Function.
Click here to see the folder structure
. ├── images │ ├── crowded_flux_subdiv.png │ ├── crowded_subdiv_example.png │ ├── ellipticity_ratio.png │ ├── flux_frac_diff.png │ ├── flux_line_plot_stamps.png │ ├── flux_subdiv.png │ ├── fwhm_ratio.png │ └── subdiv_example.png ├── pre_processing │ └── Automation.cl ├── psf │ ├── get_psf_coeffs.bash │ ├── psf_calculate.py │ ├── psfccfbrd210048_1_1.bin.txt │ ├── psfccfbrd210048_1_1_img.fits │ ├── psf_estimation.bash │ ├── psf_mat_show.ipynb │ ├── psf_steps_and_params.MD │ └── README.md ├── README.md ├── restoration │ ├── application_sgp_star_stamps.py │ ├── application_sgp_subdivisions.py │ ├── flux_conserve_proj.py │ ├── sgp.py │ ├── simulated_test │ │ ├── data │ │ │ ├── NGC7027_255.mat │ │ │ └── satellite_25500.mat │ │ └── init.py │ ├── simulation_test_sgp.py │ ├── tests.py │ └── utils.py └── results ├── CROWDED_SUBDIV_BEST_BETA_INIT.npy ├── CROWDED_SUBDIV_EXEC_TIME_BETA.npy ├── CROWDED_SUBDIV_EXEC_TIME.npy ├── CROWDED_SUBDIV_NUM_ITERS_BETA.npy ├── CROWDED_SUBDIV_NUM_ITERS.npy ├── CROWDED_SUBDIV_ORIGCAT_2sigma.csv ├── CROWDED_SUBDIV_ORIGCAT.csv ├── CROWDED_SUBDIV_ORIG_FLUX_BETA.npy ├── CROWDED_SUBDIV_ORIG_FLUX.npy ├── CROWDED_SUBDIV_ORIGIMG_BETA.fits ├── CROWDED_SUBDIV_ORIGIMG.fits ├── CROWDED_SUBDIV_RESTORED_BETA.csv ├── CROWDED_SUBDIV_RESTORED_BETA_MATCHED.csv ├── CROWDED_SUBDIV_RESTORED.csv ├── CROWDED_SUBDIV_RESTORED_FLUX_BETA.npy ├── CROWDED_SUBDIV_RESTORED_FLUX.npy ├── CROWDED_SUBDIV_RESTOREDIMG_BETA.fits ├── CROWDED_SUBDIV_RESTOREDIMG.fits ├── CROWDED_SUBDIV_RESTORED_MATCHED.csv ├── ELLIPTICITY_RATIO_BETA.npy ├── ELLIPTICITY_RATIO.npy ├── EXEC_TIME_BETA.npy ├── EXEC_TIME.npy ├── FLUX_FRACTIONAL_DIFFERENCE_BETA.npy ├── FLUX_FRACTIONAL_DIFFERENCE.npy ├── FWHM_RATIO_BETA.npy ├── FWHM_RATIO.npy ├── NUM_ITERS_BETA.npy ├── NUM_ITERS.npy ├── ORIG_FLUX_BETA.npy ├── ORIG_FLUX.npy ├── RESTORED_FLUX_BETA.npy ├── RESTORED_FLUX.npy ├── SUBDIV_BEST_BETA_INIT.npy ├── SUBDIV_EXEC_TIME_BETA.npy ├── SUBDIV_EXEC_TIME.npy ├── SUBDIV_NUM_ITERS_BETA.npy ├── SUBDIV_NUM_ITERS.npy ├── SUBDIV_ORIGCAT.csv ├── SUBDIV_ORIG_FLUX_BETA.npy ├── SUBDIV_ORIG_FLUX.npy ├── SUBDIV_ORIGIMG_BETA.fits ├── SUBDIV_ORIGIMG.fits ├── SUBDIV_RESTORED_BETA.csv ├── SUBDIV_RESTORED_BETA_MATCHED.csv ├── SUBDIV_RESTORED.csv ├── SUBDIV_RESTORED_FLUX_BETA.npy ├── SUBDIV_RESTORED_FLUX.npy ├── SUBDIV_RESTOREDIMG_BETA.fits ├── SUBDIV_RESTOREDIMG.fits ├── SUBDIV_RESTORED_MATCHED.csv ├── WD_RADIAL_PROFILE_DISTANCE_BETA.npy └── WD_RADIAL_PROFILE_DISTANCE.npy
7 directories
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sgp.pycontains implementation for SGP with both,$\beta$ -divergence and KL divergence. -
flux_conserve_proj.pycontains the flux conservation projection step code. -
utils.pycontains some utility functions helpful in other scripts.
psf_calculate.pycalculates the PSF matrix from the parameters output by thegetpsfcode from the DIAPL package1.
- It contains the results in form of metrics embedded in .npy files.
Automation.clis the IRAF automation script we generated to automate the process of removing bad bias and flat frames during the image reduction process.
Example 1:
Example 2:
The M13 globular cluster I-filter images (244 images) are available here. These images have gone through the usual image reduction pipeline.
If you find something not working as expected or anything weird, we would like to know and improve it! Please feel free to open an issue in the issue tracker or send an email to yashgondhalekar567@gmail.com
The code presented here is a modified, Python implementation of the Matlab SGP code of the SGP-dec software3. However, it is not the official Python implementation of SGP-dec.
Currently, SGP and
[1] Pych, W. Difference Image Analysis Package (DIAPL).
[2] Efficient deconvolution methods for astronomical imaging: algorithms and IDL-GPU codes M. Prato, R. Cavicchioli, L. Zanni, P. Boccacci, M. Bertero A&A 539 A133 (2012) DOI: 10.1051/0004-6361/201118681
[3] Bonettini S., Zanella R., Zanni L., 2009, InvPr, 25, 015002. doi:10.1088/0266-5611/25/1/015002
The code here is licensed under the MIT License.
Copyright (c) 2023 Yash Gondhalekar