基于量子概率统计的医学图像增强算法研究

 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.

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

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.