基于小波去噪和反卷积的光声成像优化算法
An Improved Method for Photoacoustic Image Reconstruction Based on Wavelet Denoising and Deconvolution Calculation
张鑫 1杨倩倩 1李世娜2
作者信息
- 1. 承德应用技术职业学院,河北承德 067000
- 2. 山西大学理论物理研究所,太原 030006
- 折叠
摘要
光声成像因兼具光学成像高对比度和超声成像高分辨率的优点而成为一种被广泛关注的无损成像技术.针对时间反转光声成像过程中存在的噪声污染和脉冲响应影响问题,通过建立光声压数据采集模型,将光声压数据的小波软阈值去噪和反卷积计算相结合来去除光声压数据中的噪声和脉冲响应影响.反卷积计算时设定可调节参数,使计算结果随着光声压的傅里叶系数大小不同而调整,从而进一步提升重建光声图像的质量.仿真实验结果表明,改进的时间反转光声成像算法,能有效减少重建光声图像中的噪声和伪影,明显提高重建光声图像的可视效果.
Abstract
Photoacoustic image reconstruction is widely used as a nondestructive image reconstruction method because it has high contrast like optical imaging and high resolution like ultrasonic imaging.Aiming at the problems of noise pollution and impulse response in the process of time reversal photoacoustic imaging,a model used to collect photo-acoustic pressure is built.Wavelet soft threshold denoising method and deconvolution calculation are jointly used to re-move noise and effect of the sensor impulse response.The experimental results prove that this new method can remove noise and artifacts in the reconstructed photoacoustic image effectively and visual effects of the reconstructed photo-acoustic image are significantly improved.
关键词
图像处理/光声图像重建/小波软阈值去噪/脉冲响应/反卷积Key words
image processing/photoacoustic image reconstruction/wavelet soft threshold denoising/impulse re-sponse/deconvolution calculation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据