A Tabletop Nano-CT Image Noise Reduction Network Based on 3-Dimensional Axial Attention Mechanism  

作　　者：Huijuan Fu Linlin Zhu ChunhuiWang Xiaoqi Xi Yu Han Lei Li Yanmin Sun Bin Yan 

机构地区：[1]Henan Key Laboratory of Imaging and Intelligent Processing,PLA Strategic Support Force Information Engineering University,Zhengzhou,450000,China

出　　处：《Computers, Materials & Continua》2024年第7期1711-1725,共15页计算机、材料和连续体（英文）

基　　金：supported by the National Natural Science Foundation of China(62201618).

摘　　要：Nano-computed tomography(Nano-CT)is an emerging,high-resolution imaging technique.However,due to their low-light properties,tabletop Nano-CT has to be scanned under long exposure conditions,which the scanning process is time-consuming.For 3D reconstruction data,this paper proposed a lightweight 3D noise reduction method for desktop-level Nano-CT called AAD-ResNet(Axial Attention DeNoise ResNet).The network is framed by theU-net structure.The encoder and decoder are incorporated with the proposed 3D axial attention mechanism and residual dense block.Each layer of the residual dense block can directly access the features of the previous layer,which reduces the redundancy of parameters and improves the efficiency of network training.The 3D axial attention mechanism enhances the correlation between 3D information in the training process and captures the long-distance dependence.It can improve the noise reduction effect and avoid the loss of image structure details.Experimental results show that the network can effectively improve the image quality of a 0.1-s exposure scan to a level close to a 3-s exposure,significantly shortening the sample scanning time.

关 键 词：Deep learning tabletop Nano-CT image denoising 3D axial attention mechanism 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]