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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 2024, 80(1), 1711-1725. https://doi.org/10.32604/cmc.2024.049623

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.

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APA Style
Fu, H., Zhu, L., Wang, C., Xi, X., Han, Y. et al. (2024). A tabletop nano-ct image noise reduction network based on 3-dimensional axial attention mechanism. Computers, Materials & Continua, 80(1), 1711-1725. https://doi.org/10.32604/cmc.2024.049623
Vancouver Style
Fu H, Zhu L, Wang C, Xi X, Han Y, Li L, et al. A tabletop nano-ct image noise reduction network based on 3-dimensional axial attention mechanism. Comput Mater Contin. 2024;80(1):1711-1725 https://doi.org/10.32604/cmc.2024.049623
IEEE Style
H. Fu et al., “A Tabletop Nano-CT Image Noise Reduction Network Based on 3-Dimensional Axial Attention Mechanism,” Comput. Mater. Contin., vol. 80, no. 1, pp. 1711-1725, 2024. https://doi.org/10.32604/cmc.2024.049623



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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