Python Inpainter for Cosmological and AStrophysical SOurces¶
This package provides a suite of inpainting methodologies aimed at reconstructing holes on images (128x128 pixels) extracted from a HEALPIX map.
Three inpainting techniques are included in
PICASSO and can be divided into two main groups:
diffusive-based methods (Nearest-Neighbours)
learning-based methods that rely on training DCNNs to fill the missing pixels with the predictions learned from a training data-set (Deep-Prior and Generative Adversarial Networks, GAN ).
For further details see Puglisi et al. (2020).
PICASSO has been tested on inpainting maps of two polarized emissions in the microwave regime: Synchrotron and Thermal Dust. GAN weights have been derived by training on images of each emission and can be found at : GAN weights.
git clone https://github.com/giuspugl/picasso cd picasso python setup.py install
Scripts are provided to the user in order to perform:
projection from full sky HEALPIX maps to flat thumbnails images image_stacker
inpainting on GPUs inpaint_gpu
parallel inpainting on multiple processes (with
projection from flat images to HEALPIX inpaint_gpu
An example of a crop images (leftmost panel) inpainted with the three methods in
If you encounter any difficulty in installing and using the code or you think you found a bug, please open an issue.