  Non-blind image deconvolution has been studied for several decades but most
of the existing work focuses on blur instead of noise. In photon-limited
conditions, however, the excessive amount of shot noise makes traditional
deconvolution algorithms fail. In searching for reasons why these methods fail,
we present a systematic analysis of the Poisson non-blind deconvolution
algorithms reported in the literature, covering both classical and deep
learning methods. We compile a list of five "secrets" highlighting the do's and
don'ts when designing algorithms. Based on this analysis, we build a
proof-of-concept method by combining the five secrets. We find that the new
method performs on par with some of the latest methods while outperforming some
older ones.
