Huijuan Fu, Linlin Zhu, Chunhui Wang, Xiaoqi Xi, Yu Han, Lei Li, Yanmin Sun, Bin Yan*
CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1711-1725, 2024, DOI: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 More >
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