Post-processing denoising of low-dose CT images based on GAN network

Siyuan Zhang; Jian Dong; Shuang Li

doi:10.1117/12.3029731

21 June 2024 Post-processing denoising of low-dose CT images based on GAN network

Siyuan Zhang, Jian Dong, Shuang Li

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131672K (2024) https://doi.org/10.1117/12.3029731
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

In recent years, the damage of X-rays to the human body has received widespread attention. This paper analyzes and improves the image super-resolution reconstruction algorithm based on adversarial-generative networks that performs well in the field of image post-processing. This algorithm can largely weaken the The loss function in the convolutional network directly manages the defects of the result, thereby achieving the purpose of restoring image details more clearly. We have purposefully added structures such as filtering, feature extraction, and detail restoration to the generation network to achieve a state suitable for low-dose CT image restoration. Experimental results show that this kind of network has a more stable reconstruction effect and can obtain clearer texture and detail information.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Siyuan Zhang, Jian Dong, and Shuang Li "Post-processing denoising of low-dose CT images based on GAN network", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131672K (21 June 2024); https://doi.org/10.1117/12.3029731

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