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.

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Lens-coupled high-resolution micro-CT uses a visible light magnification system behind the X-ray path to achieve higher resolution imaging than conventional micro-CT. However, the spatial resolution is theoretically limited by optical diffraction and mechanical control precision. As a result, the current system resolution is still insufficient for some applications, such as the imaging of biological materials whose structures are on the nanometer scale. To overcome this limitation, a super-resolution algorithm can be employed to improve the image resolution beyond the theoretical upper bound of the ideal spatial resolution of the system. In this work, a super-resolution model-based iterative reconstruction (SR-MBIR) algorithm is proposed based on a lens-coupled high-resolution micro-CT system and a high-precision nano-stage attached to the rotation stage of the system. The algorithm employs a scanning program that dithers the object via the nano-stage to obtain multiple sets of projection images with sub-pixel information. The blur and noise statistical models are introduced into the physical model for iterative reconstruction, allowing for super-resolution, deblurring, and noise suppression. The results of simulation data and actual data show that the SR-MBIR algorithm has a prominent effect in improving image resolution. The reconstructed images have sharper edges, better details, higher signal-to-noise ratio, and can effectively suppress the systematic blur and noise in the imaging process, thus achieving superior interior reconstruction quality.