  Self-supervised image denoising techniques emerged as convenient methods that
allow training denoising models without requiring ground-truth noise-free data.
Existing methods usually optimize loss metrics that are calculated from
multiple noisy realizations of similar images, e.g., from neighboring
tomographic slices. However, those approaches fail to utilize the multiple
contrasts that are routinely acquired in medical imaging modalities like MRI or
dual-energy CT. In this work, we propose the new self-supervised training
scheme Noise2Contrast that combines information from multiple measured image
contrasts to train a denoising model. We stack denoising with domain-transfer
operators to utilize the independent noise realizations of different image
contrasts to derive a self-supervised loss. The trained denoising operator
achieves convincing quantitative and qualitative results, outperforming
state-of-the-art self-supervised methods by 4.7-11.0%/4.8-7.3% (PSNR/SSIM) on
brain MRI data and by 43.6-50.5%/57.1-77.1% (PSNR/SSIM) on dual-energy CT X-ray
microscopy data with respect to the noisy baseline. Our experiments on
different real measured data sets indicate that Noise2Contrast training
generalizes to other multi-contrast imaging modalities.
