基于生成对抗网络的光学层析成像方法  

Reconstruction Method for Optical Tomography Based on Generative Adversarial Network

作　　者：徐依婷 李华军 朱映旷 陈连杰 章有虎 Xu Yiting;Li Huajun;Zhu Yingkuang;Chen Lianjie;Zhang Youhu(School of Automation,Hangzhou Dianzi University,Hangzhou 310016,Zhejiang,China;Hangzhou Zhongtai Cryogenic Technology Corporation,Hangzhou 311402,Zhejiang,China)

机构地区：[1]杭州电子科技大学自动化学院,浙江杭州310016 [2]杭州中泰深冷技术股份有限公司,浙江杭州311402

出　　处：《激光与光电子学进展》2024年第12期81-92,共12页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(51906053)。

摘　　要：针对光学层析成像中重建图像伪影重、噪声大、耗时长的问题,基于生成对抗网络提出一种LBP-Pix2Pix图像重建方法。通过线性反投影法(LBP)对物体截面吸收系数分布进行初步重建,将初步重建图像与真实分布作为Pix2Pix模型的训练样本,通过生成器与判别器的对抗训练,获得最优重建模型。利用模型对LBP重建图像进行处理,得到伪影较少、边缘清晰的重建图像。对5种截面分布进行测试,结果表明,LBP-Pix2Pix方法重建误差范围为5.20%~13.15%,相关系数范围为88.34%~99.08%。相较于其他重建方法,该方法成像速度显著提高,图像准确度明显提升,为光学层析成像提供了一种新的图像重建方案。A linear back projection Pix2Pix(LBP-Pix2Pix)image reconstruction method,based on generative adversarial networks,is proposed to address the issues of heavy artifacts,high noise levels,and long processing times in optical tomography reconstruction.This method utilizes the LBP technique to reconstruct the absorption coefficient distribution within the object’s cross-section.The initial reconstructed image and the true distribution are used as training samples for the Pix2Pix model.The optimal reconstruction model is obtained through adversarial training of the generator and discriminator.Using the model to process LBP-reconstructed images yields reconstructed images with fewer artifacts and clear edges.Five cross-sectional distributions are tested,and the results show that the reconstruction error range of the LBP-Pix2Pix method is 5.20%~13.15%,and the correlation coefficient range is 88.34%~99.08%.Compared with other reconstruction techniques,this method significantly enhances the imaging speed and image accuracy,presenting a novel image reconstruction approach for optical tomography.

关 键 词：光学层析成像 重建算法 生成对抗网络 图像处理 深度学习 

分 类 号：TP317.4[自动化与计算机技术—计算机软件与理论]