Positive Iterative Deconvolution in Comparison to Richardson-Lucy Like Algorithms

Positive iterative deconvolution is an algorithm that applies non-linear constraints, conserves energy, and delivers stable results at high noise-levels. This is also true for Richardson-Lucy like algorithms which follow a statistical approach to the deconvolution problem. In two-dimensional computer experiments, star-like and planet-like objects are convolved with band-limited point-spread functions. Photon noise and read-out noise are applied to these images as well as to the pointspread functions being used for deconvolution. Why Richardson-Lucy like algorithms favor star-like objects and the difference in computational efforts are discussed.