A study on low-dose CT image denoising method based on similar block learning

Huijuan Fu; Xiaoqi Xi; Yu Han; Linlin Zhu; Mengnan Liu; Siyu Tan; Chang Liu; Lei Li; Bin Yan

doi:10.1117/12.2689480

10 August 2023 A study on low-dose CT image denoising method based on similar block learning

Huijuan Fu, Xiaoqi Xi, Yu Han, Linlin Zhu, Mengnan Liu, Siyu Tan, Chang Liu, Lei Li, Bin Yan

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127480F (2023) https://doi.org/10.1117/12.2689480
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

X-ray tomographic imaging has become an important analytical tool with a wide range of applications. It is inevitable that noise is introduced in CT images, and noise reduction is necessary. To solve this problem, we considered to use the nonlocal property of similar block search and proposed a deep learning network based on similar block learning for noise reduction of micro CT short exposure time scanned images to improve the scanning efficiency while ensuring high quality imaging. The method uses the output of the nonlocal method as a data preprocessing algorithm by combining a nonlocal block matching algorithm with a convolutional neural network, and uses a residual channel attention mechanism to learn the features after feature extraction, which reduces noise while preserving image details. Experimental results show that the method can remove noise from CT images quickly and effectively, and compared with the classical CPCE noise reduction method, the method improves the PSNR index by 1.52 dB, which is consistent with the theoretical assumption.

Citation Download Citation

Huijuan Fu, Xiaoqi Xi, Yu Han, Linlin Zhu, Mengnan Liu, Siyu Tan, Chang Liu, Lei Li, and Bin Yan "A study on low-dose CT image denoising method based on similar block learning", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127480F (10 August 2023); https://doi.org/10.1117/12.2689480

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