The Secrets of Non-Blind Poisson Deconvolution

TL;DR

This paper systematically analyzes Poisson non-blind deconvolution algorithms, revealing key insights and proposing a new method that performs competitively in photon-limited imaging conditions.

Contribution

It provides a comprehensive analysis of existing algorithms, identifies five key principles, and introduces a new method that matches or exceeds current performance.

Findings

The new method performs on par with state-of-the-art algorithms.

It outperforms some older deconvolution methods.

Systematic analysis reveals critical design secrets for Poisson deconvolution.

Abstract

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.