Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching
With the rapid development of deep learning, supervised convolutional neural network methods have greatly improved the performance of low-dose computed tomography (CT) denoising.
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Key points
- Some classic self-supervised denoising methods rely on the assumption of pixel-wise independent noise, making them ineffective for correlated noise in low-dose CT (LDCT) images.
- In this paper, we propose Noise2Match, a self-supervised LDCT denoising method based on adaptive inter-slice matching.
- Noise2Match introduces an inter-slice matching network to model the label transition process between adjacent slices, and then applies correction to the denoiser's outputs to better satisfy the equal-mean condition.
- We further find that Noise2Match can also effectively perform low-dose conebeam CT (CBCT) denoising, which could be attributed to its correction operation that reduces inter-slice noise correlation.
Sources (1)
- [1]Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice MatchingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 01:32 PM
With the rapid development of deep learning, supervised convolutional neural network methods have greatly improved the performance of low-dose computed tomography (CT) denoising.
Some classic self-supervised denoising methods rely on the assumption of pixel-wise independent noise, making them ineffective for correlated noise in low-dose CT (LDCT) images.
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