Research on Image Enhancement Algorithms of Medical Images Based on Quantum Probability Statistics

FONT face; Verdana; FU Xiao-wei; DING Ming-yue; ZHOU Cheng-ping; CAI Chao; SUN Yang-guang; FONT

您当前的位置：

首页 >

文章列表页 >

Research on Image Enhancement Algorithms of Medical Images Based on Quantum Probability Statistics

更新时间：2025-07-16

- Research on Image Enhancement Algorithms of Medical Images Based on Quantum Probability Statistics
- Acta Electronica Sinica Vol. 38, Issue 7, Pages: 1590-1596(2010)
- 作者机构：
  
  1. 华中科技大学图像识别与人工智能研究所,湖北,武汉,430074
  2. 武汉科技大学计算机科学与技术学院,湖北,武汉,430065
  3. 华中科技大学生命科学与技术学院,湖北,武汉,430074
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN911.73
- Published：2010
- 稿件说明：
移动端阅览
FONT face, Verdana, FU Xiao-wei, et al. Research on Image Enhancement Algorithms of Medical Images Based on Quantum Probability Statistics[J]. Acta Electronica Sinica, 2010, 38(7): 1590-1596.
DOI：

FONT face, Verdana, FU Xiao-wei, et al. Research on Image Enhancement Algorithms of Medical Images Based on Quantum Probability Statistics[J]. Acta Electronica Sinica, 2010, 38(7): 1590-1596. DOI：

摘要

<FONT face=Verdana>传统的医学图像增强算法存在对噪声敏感且易陷入欠增强或过增强等不足. 本文首先利用量子信号处理基本原理，定义了两种不同的像素量子比特表达形式；然后，针对医学图像的特点，结合3×3邻域像素灰度相关性，提出了一种基于量子概率统计的图像增强算子. 为了优化图像增强的效果，根据子采样图像信息熵自适应确定本算子的灰度阈值参数. 通过主观和客观评价，实验结果表明本文所提出的增强方法考虑了图像全局与局部信息，能更有效地提高医学图像质量.

Abstract

<FONT face=Verdana>Traditional image enhancement algorithms are sensitive to the noise and can easily fall into a sub or over enhancement for medical images. In this paper

two different mathematics expressions of pixel quantum bit are given first according to the basic principle of quantum signal processing. Then

aiming at the characteristics of medical images and combining with gray correlative characteristics of pixels in 3×3 neighborhoods

a medical image enhancement operator is proposed based on quantum probability statistics. In order to optimize the effect of image enhancement

the gray threshold parameter of the operator is adaptively chosen based on the sub-sampling image entropy. Using subjective and objective evaluation

experimental results demonstrated that our method considered both global and local image information and can improve medical images quality effectively.

关键词

Keywords

references

Views

2150

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Image Enhancement via Content Semantic-Aware Multimodal Fusion

Progressive Edge-Aware Interactive Network for Multi-Degraded Low-Light Image Enhancement

Quality Assessment of Light Field Images Based on Contrastive Visual-Textual Model

Infrared Image Enhancement Based on Multi-Scale Cyclic Convolution and Multi-Clustering Space

Dynamic Multi-Histogram Equalization Based on Fast Fuzzy Clustering

Related Author

ZHU Han-cheng

LIU Xin-yu

YAO Rui

SHAO Zhi-wen

ZHOU Yong

LI Lei-da

LI Yue-zhou

NIU Yu-zhen

Related Institution

School of Artificial Intelligence, Xidian University

Mine Digitization Engineering Research Center of the Ministry of Education

School of Computer Science and Technology / School of Artificial Intelligence, China University of Mining and Technology

College of Photonic and Electronic Engineering, Fujian Normal University

College of Computer and Data Science, Fuzhou University

⁰