WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising
<|reference_start|>WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising: In clinical examinations and diagnoses, low-dose computed tomography (LDCT) is crucial for minimizing health risks compared with normal-dose computed tomography (NDCT). However, reducing the radiation dose compromises the signal-to-noise ratio, leading to degraded quality of CT images. To address this, we analyze LDCT denoising task based on experimental results from the frequency perspective, and then introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data. The proposed WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM). First, WIA is introduced to align NDCT with LDCT by mainly adding noise to the high-frequency components, which is the main difference between LDCT and NDCT. Second, to better capture high-frequency components and detailed information, Frequency-Aware Multi-scale Loss (FAM) is proposed by effectively utilizing multi-scale feature space. Extensive experiments on two public LDCT denoising datasets demonstrate that our WIA-LD2ND, only uses NDCT, outperforms existing several state-of-the-art weakly-supervised and self-supervised methods. Source code is available at https://github.com/zhaohaoyu376/WI-LD2ND.<|reference_end|>
@article{zhao2024wia-ld2nd:,
    title={WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT
  Denoising},
    author={Haoyu Zhao, Yuliang Gu, Zhou Zhao, Bo Du, Yongchao Xu, Rui Yu},
    journal={arXiv preprint arXiv:2403.11672},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11672},
    primaryClass={eess.IV cs.CV}
}