ASTROALIGN is a python module that will try to align two stellar astronomical images, especially when there is no WCS information available.

It does so by finding similar 3-point asterisms (triangles) in both images and deducing the affine transformation between them.

Generic registration routines try to match feature points, using corner detection routines to make the point correspondence. These generally fail for stellar astronomical images, since stars have very little stable structure and so, in general, indistinguishable from each other. Asterism matching is more robust, and closer to the human way of matching stellar images.

Astroalign can match images of very different field of view, point-spread function, seeing and atmospheric conditions.

It may not work, or work with special care, on images of extended objects with few point-like sources or in very crowded fields.

You can find a Jupyter notebook example with the main features at http://quatrope.github.io/astroalign/.

Full documentation: https://astroalign.readthedocs.io/

Installation

Using setuptools:

$ pip install astroalign

or from this distribution with

$ python setup.py install

Performance: Optional

This library is optionally compatible with bottleneck and may offer performance improvements in some cases. Install bottleneck in your project as a peer to astroalign using:

pip install bottleneck

Astroalign will pick this optional dependency up and use it's performance improved functions for computing transforms.

Running Tests

python tests/test_align.py

Usage example

>>> import astroalign as aa
>>> aligned_image, footprint = aa.register(source_image, target_image)

In this example source_image will be interpolated by a transformation to coincide pixel to pixel with target_image and stored in aligned_image.

If we are only interested in knowing the transformation and the correspondence of control points in both images, use find_transform will return the transformation in a Scikit-Image SimilarityTransform object and a list of stars in source with the corresponding stars in target.

>>> transf, (s_list, t_list) = aa.find_transform(source, target)

source and target can each either be the numpy array of the image (grayscale or color), or an iterable of (x, y) pairs of star positions on the image.

The returned transf object is a scikit-image SimilarityTranform object that contains the transformation matrix along with the scale, rotation and translation parameters.

s_list and t_list are numpy arrays of (x, y) point correspondence between source and target. transf applied to s_list will approximately render t_list.

Citation

If you use astroalign in a scientific publication, we would appreciate citations to the following paper:

Astroalign: A Python module for astronomical image registration.
Beroiz, M., Cabral, J. B., & Sanchez, B.
Astronomy & Computing, Volume 32, July 2020, 100384.

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