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We present an algorithm implemented in the astroalign Python module for image registration in astronomy. Our module does not rely on WCS information and instead matches 3-point asterisms (triangles) on the images to find the most accurate linear transformation between the two. It is especially useful in the context of aligning images prior to stacking or performing difference image analysis. Astroalign can match images of different point-spread functions, seeing, and atmospheric conditions.
Deformable image registration (DIR) is essential for many image-guided therapies. Recently, deep learning approaches have gained substantial popularity and success in DIR. Most deep learning approaches use the so-called mono-stream high-to-low, low-t
astroquery is a collection of tools for requesting data from databases hosted on remote servers with interfaces exposed on the internet, including those with web pages but without formal application program interfaces (APIs). These tools are built on
scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Py
DeepReg (https://github.com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.
I describe a new, open-source astronomical image-fitting program called Imfit, specialized for galaxies but potentially useful for other sources, which is fast, flexible, and highly extensible. A key characteristic of the program is an object-oriente