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Open Access

ARTICLE

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

Huijuan Fu, Linlin Zhu, Chunhui Wang, Xiaoqi Xi, Yu Han, Lei Li, Yanmin Sun, Bin Yan*
Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, 450000, China
* Corresponding Author: Bin Yan. Email: email
(This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.049623

Received 12 January 2024; Accepted 28 April 2024; Published online 20 June 2024

Abstract

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 the U-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.

Keywords

Deep learning; tabletop Nano-CT; image denoising; 3D axial attention mechanism

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