PSF Homogenization kERnels -
Compute an homogenization kernel between two PSFs
This code is well suited for PSF matching applications in both an astronomical or microscopy context.
- Warp (rotation + resampling) the PSF images (if necessary),
- Filter images in Fourier space using a regularized Wiener filter,
- Produce a homogenization kernel.
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.
This code does not
- interpolate NaN values (replaced by 0 instead),
- center PSF images,
- minimize the kernel size.
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
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”
submitted to Astronomy & Astrophysics journal
|Email:||alexandre.boucaud [at] ias.u-psud.fr|
|Licence:||This work is licensed under a 3-clause BSD license|