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PSF fitting photometry allows a simultaneously fit of a PSF profile on the sources. Many routines use PSF fitting photometry, including IRAF/allstar, Strarfinder, and Convphot. These routines are in general complex to use and slow. FASTPHOT is optimized for prior extraction (the position of the sources is known) and is very fast and simple.
This IDL library is designed to be used on astronomical images. Its main aim is to stack data to allow a statistical detection of faint signal, using a prior. For instance, you can stack 160um data using the positions of galaxies detected at 24um or 3.6um, or use WMAP sources to stack Planck data. It can estimate error bars using bootstrap, and it can perform photometry (aperture photometry, or PSF fitting, or other that you can plug). The IAS Stacking Library works with gnomonic projections (RA---TAN), and also with HEALPIX projection.
PyPHER (Python-based PSF Homogenization kERnels) computes an homogenization kernel between two PSFs; the code is well-suited for PSF matching applications in both an astronomical or microscopy context. It can warp (rotation + resampling) the PSF images (if necessary), filter images in Fourier space using a regularized Wiener filter, and produce a homogenization kernel. PyPHER requires the pixel scale information to be present in the FITS files, which can if necessary be added by using the provided ADDPIXSCL method.