# PSF Homogenization kERnels - pypher¶

Compute an homogenization kernel between two PSFs

This code is well suited for PSF matching applications in both an astronomical or microscopy context.

It has been developed as part of the ESA Euclid mission and is currently being used for multi-band photometric studies of HST (visible) and Herschel (IR) data.

## Features¶

1. Warp (rotation + resampling) the PSF images (if necessary),
2. Filter images in Fourier space using a regularized Wiener filter,
3. Produce a homogenization kernel.

Note: pypher needs the pixel scale information to be present in the FITS files. If not, use the provided addpixscl method to add this missing info.

Warning

This code does not

• interpolate NaN values (replaced by 0 instead),
• center PSF images,
• minimize the kernel size.

## Quick setup¶

In most cases the code can be installed via

$pip install pypher  and a kernel can then be produced from two PSFs with a simple command line $ pypher psf_a.fits psf_b.fits kernel_a_to_b.fits


Other installation procedures are described on the installation page and further command line options here.

## Acknowledging¶

If you make use of any product of this code in a scientific publication, please consider acknowledging the work by citing the following paper

Boucaud et al. (2016) “Convolution kernels for multi-wavelength imaging”

Note

submitted to Astronomy & Astrophysics journal