Comparative analysis of Richardson-Lucy deconvolution and data unfolding with mean integrated square error optimization

IF 3.4 2区物理与天体物理 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Physics Communications Pub Date : 2025-10-08 DOI:10.1016/j.cpc.2025.109894

Nikolay D. Gagunashvili

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引用次数: 0

Abstract

Two maximum likelihood-based algorithms for unfolding or deconvolution are considered: the Richardson-Lucy method and the Data Unfolding method with Mean Integrated Square Error (MISE) optimization. Unfolding is viewed as a procedure for estimating an unknown probability density function. Both external and internal quality assessment methods can be applied for this purpose. In some cases, external criteria exist to evaluate deconvolution quality. A typical example is the deconvolution of a blurred image, where the sharpness of the restored image serves as an indicator of quality. However, defining such external criteria can be challenging, particularly when a measurement has not been performed previously. In such instances, internal criteria are necessary to assess the quality of the result independently of external information. The article discusses two internal criteria: MISE for the unfolded distribution and the condition number of the correlation matrix of the unfolded distribution. These internal quality criteria are applied to a comparative analysis of the two methods using identical numerical data. The results of the analysis demonstrate the superiority of the Data Unfolding method with MISE optimization over the Richardson-Lucy method.

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Richardson-Lucy反卷积与均值平方误差优化数据展开的对比分析

考虑了两种基于最大似然的展开或反卷积算法：Richardson-Lucy方法和具有平均积分平方误差（MISE）优化的数据展开方法。展开被看作是一个估计未知概率密度函数的过程。为此，可以采用外部和内部质量评价方法。在某些情况下，存在外部标准来评估反褶积质量。一个典型的例子是模糊图像的反卷积，其中恢复图像的清晰度作为质量的指标。然而，定义这样的外部标准可能是具有挑战性的，特别是当以前没有执行度量时。在这种情况下，有必要采用独立于外部信息的内部标准来评估结果的质量。本文讨论了展开分布的两个内部准则：展开分布的MISE和展开分布的相关矩阵的条件数。这些内部质量标准应用于使用相同数值数据的两种方法的比较分析。分析结果表明，基于MISE优化的数据展开方法优于Richardson-Lucy方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Physics Communications 物理-计算机：跨学科应用

CiteScore

12.10

自引率

3.20%

发文量

287

审稿时长

5.3 months

期刊介绍： The focus of CPC is on contemporary computational methods and techniques and their implementation, the effectiveness of which will normally be evidenced by the author(s) within the context of a substantive problem in physics. Within this setting CPC publishes two types of paper. Computer Programs in Physics (CPiP) These papers describe significant computer programs to be archived in the CPC Program Library which is held in the Mendeley Data repository. The submitted software must be covered by an approved open source licence. Papers and associated computer programs that address a problem of contemporary interest in physics that cannot be solved by current software are particularly encouraged. Computational Physics Papers (CP) These are research papers in, but are not limited to, the following themes across computational physics and related disciplines. mathematical and numerical methods and algorithms; computational models including those associated with the design, control and analysis of experiments; and algebraic computation. Each will normally include software implementation and performance details. The software implementation should, ideally, be available via GitHub, Zenodo or an institutional repository.In addition, research papers on the impact of advanced computer architecture and special purpose computers on computing in the physical sciences and software topics related to, and of importance in, the physical sciences may be considered.