基于最小二乘准则的模糊估计和图像复原

卿粼波; 何小海; 陶青川

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基于最小二乘准则的模糊估计和图像复原

信息工程 | 更新时间：2023-11-28

- 基于最小二乘准则的模糊估计和图像复原
- Blur Identification and 3D Image Restoration Based on Least square Theory
- 工程科学与技术 2008年40卷第2期页码：129-133
- 作者机构：
  
  四川大学电子信息学院,图像信息研究所四川,成都,610064
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目（60372079）
- DOI：
  中图分类号：
- 纸质出版日期：2008，
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卿粼波,何小海,陶青川.基于最小二乘准则的模糊估计和图像复原[J].工程科学与技术,2008,40(2):129-133.

Blur Identification and 3D Image Restoration Based on Least square Theory[J]. Advanced Engineering Sciences, 2008,40(2):129-133.
卿粼波,何小海,陶青川.基于最小二乘准则的模糊估计和图像复原[J].工程科学与技术,2008,40(2):129-133. DOI：

Blur Identification and 3D Image Restoration Based on Least square Theory[J]. Advanced Engineering Sciences, 2008,40(2):129-133. DOI：

摘要

中文摘要: 在计算光学显微成像技术中，点扩展函数往往是未知的，且不易获取，从而给图像复原带来很大困难。基于最小二乘准则和最优化理论，提出了利用变尺度法的三维点扩展函数参数估计算法；针对传统EM算法存在复原效果细节丢失严重等问题，提出最小二乘共轭梯度三维图像复原算法。算法在点扩展函数参数估计和求解真实图像之间进行交替迭代，从而得到图像的最优估计。实验表明，新算法在较短时间内，能够较准确地估计出点扩展函数参数，并得到较好的复原结果。

Abstract

Abstract:The point spread function（PSF）in computational optical sectioning microscopy (COSM) technology was always unknown and hard to obtain. This brought a lot of difficulties to image restoration. Based on least square and optimal theory

a parameter estimation method using variable metric was proposed for 3D point spread function. In addition

to overcome the traditional EM algorithm’s limit such as serious detail loss

a conjugate gradient least square(CGLS) algorithm for the restoration of 3D images was developed. The optimal real image estimation was obtained through alterative iteration between the parameter estimation of point spread function and the real images estimation. Experimental results showed that the new algorithm could successfully identify blurs and restore images

in a short time.

关键词

计算光学显微成像最小二乘模糊估计图像复原

Keywords

computational optical sectioning microscopyleast squareblur identificationimage restoration

references

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