  The spatial resolution of astronomical images is limited by atmospheric
turbulence and diffraction in the telescope optics, resulting in blurred
images. This makes it difficult to accurately measure the brightness of blended
objects because the contributions from adjacent objects are mixed in a
time-variable manner due to changes in the atmospheric conditions. However,
this effect can be corrected by characterizing the Point Spread Function (PSF),
which describes how a point source is blurred on a detector. This function can
be estimated from the stars in the field of view, which provides a natural
sampling of the PSF across the entire field of view.
  Once the PSF is estimated, it can be removed from the data through the
so-called deconvolution process, leading to images of improved spatial
resolution. The deconvolution operation is an ill-posed inverse problem due to
noise and pixelization of the data. To solve this problem, regularization is
necessary to guarantee the robustness of the solution. Regularization can take
the form of a sparse prior, meaning that the recovered solution can be
represented with only a few basis eigenvectors.
  STARRED is a Python package developed in the context of the COSMOGRAIL
collaboration and applies to a vast variety of astronomical problems. It
proposes to use an isotropic wavelet basis, called Starlets, to regularize the
solution of the deconvolution problem. This family of wavelets has been shown
to be well-suited to represent astronomical objects. STARRED provides two
modules to first reconstruct the PSF, and then perform the deconvolution. It is
based on two key concepts: i) the image is reconstructed in two separate
channels, one for the point sources and one for the extended sources, and ii)
the code relies on the deliberate choice of not completely removing the effect
of the PSF, but rather bringing the image to a higher resolution.
