STARRED: a two-channel deconvolution method with Starlet regularization

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