Micro-CT image denoising with an asymmetric perceptual convolutional network. (2nd July 2021)

• Record Type: Journal Article
• Authors: Yao, Weiguo; Chen, Lujie; Wu, Huiming; Zhao, Qingxian; Luo, Shouhua
• Is Part Of: Physics in medicine & biology. Volume 66:Number 13(2021)
• Publication Date: 2021-07-02
• Subjects: Micro-CT -- asymmetric convolution -- edge detection -- image domain denoising; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac0bd2
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Micro-CT has important applications in biomedical research due to its ability to perform high-precision 3D imaging of micro-architecture in a non-invasive way. Because of the limited power of the radiation source, it is difficult to obtain a high signal-to-noise image under the requirement of temporal resolution. Therefore, low-dose CT image denoising has attracted considerable attention to improve the image quality of micro-CT while maintaining time resolution. In this paper, an end-to-end asymmetric perceptual convolutional network (APCNet) is proposed to enhance the network's ability to capture and retain image details by improving the convolutional layer and introducing an edge detection layer. Compared with the previously proposed denoising models such as DnCNN, CNN-VGG, and RED-CNN, experiments proved that our proposed method has achieved better results in both numerical indicators and visual perception.