Multi-Scale Feature Fusion Network for Low-Dose CT Denoising

Li, Zhiyuan; Liu, Yi; Hu, Shu; Lü, Jing; Kang, Jiaqi; Chen, Yang; Gui, Zhiguo

doi:10.1007/s10278-023-00805-0

Cited by 6 publications

(1 citation statement)

References 56 publications

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“…Zou et al designed a dual attention to adaptively reinforce important channels and spatial regions [ 34 ]. Li et al proposed a multi-scale feature fusion network for CT image denoising by combining multiple feature extraction modules [ 35 ]. In the multi-scale feature extraction blocks of our proposed MAGUNet model, the residual dilated convolution blocks were utilized to balance the number of parameters and the performance of feature extraction.…”

Section: Related Workmentioning

confidence: 99%

A Residual UNet Denoising Network Based on Multi-Scale Feature Extraction and Attention-Guided Filter

Liu

Lin

et al. 2023

Sensors

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In order to obtain high-quality images, it is very important to remove noise effectively and retain image details reasonably. In this paper, we propose a residual UNet denoising network that adds the attention-guided filter and multi-scale feature extraction blocks. We design a multi-scale feature extraction block as the input block to expand the receiving domain and extract more useful features. We also develop the attention-guided filter block to hold the edge information. Further, we use the global residual network strategy to model residual noise instead of directly modeling clean images. Experimental results show our proposed network performs favorably against several state-of-the-art models. Our proposed model can not only suppress the noise more effectively, but also improve the sharpness of the image.

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Section: Related Workmentioning

confidence: 99%