A CT Image Denoising Method Based on Projection Domain Feature
Mengyu Sun, Dimeng Xia, Shusen Zhao, Weibin Zhang, Yaobin He
TL;DR
The paper tackles noise in industrial CT projections when increasing exposure or sampling is restricted. It proposes a projection-domain denoising approach that exploits similarity between neighboring views and uses singular value decomposition on grouped projections, complemented by eigenvalue screening via a threshold $\varepsilon$ to suppress noise while preserving structure, with reconstructions performed by SART. Key contributions include a practical, prior-free denoising module that reduces computation relative to iterative methods and can act as a plug-in to enhance other denoising techniques, demonstrated on both simulated and real battery CT data. The work offers a path to higher-quality CT reconstructions in industrial settings with limited acquisition time or dose, without requiring large training datasets.
Abstract
In order to improve image quality of projection in industrial applications, generally, a standard method is to increase the current or exposure time, which might cause overexposure of detector units in areas of thin objects or backgrounds. Increasing the projection sampling is a better method to address the issue, but it also leads to significant noise in the reconstructed image. This paper proposed a projection domain denoising algorithm based on the features of the projection domain for this case. This algorithm utilized the similarity of projections of neighboring veiws to reduce image noise quickly and effectively. The availability of the algorithm proposed in this work has been conducted by numerical simulation and practical data experiments.