# Progressive Masked Refinement Self-supervised Learning for Low-Dose CT Denoising

arXiv: https://arxiv.org/abs/2601.14180 · HTML: https://mlsanity.com/paper/2601.14180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Noise2Self: Blind Denoising by Self-Supervision](https://mlsanity.com/paper/1901.11365/) (2019)
- [Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images](https://mlsanity.com/paper/2101.02824/) (2021)
- [High-Quality Self-Supervised Deep Image Denoising](https://mlsanity.com/paper/1901.10277/) (2019)
- [Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images](https://mlsanity.com/paper/2106.07009/) (2021)
- [Unpaired Learning of Deep Image Denoising](https://mlsanity.com/paper/2008.13711/) (2020)
- [CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising](https://mlsanity.com/paper/2202.13517/) (2022)
- [Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising](https://mlsanity.com/paper/2305.15887/) (2023)
- [Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction](https://mlsanity.com/paper/2506.22012/) (2025)
- [Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising](https://mlsanity.com/paper/2308.06746/) (2023)
- [Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction](https://mlsanity.com/paper/2405.17167/) (2024)
- [Unsupervised Real-World Denoising: Sparsity is All You Need](https://mlsanity.com/paper/2503.21377/) (2025)

## Abstract (arXiv)

Self-supervised learning has been increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence on paired normal-dose CT (NDCT) data, which are often difficult to obtain. However, many existing self-supervised blind-spot denoising methods may under-utilize pixel-wise supervisory information loss due to evaluating the training loss only at masked locations. To mitigate this issue, we propose a novel Progressive Masked Refinement Learning framework that progressively refines denoising results while preserving and exploiting available LDCT information. Specifically, we explicitly inject a combination of controlled Gaussian and Poisson noise during training to regularize the denoising process and mitigate trivial identity mapping. Furthermore, we introduce a step-wise mask denoising mechanism that gradually reduces the discrepancy between synthetic corruption and the noise characteristics of LDCT images, enabling more fine-grained learning for denoising. Extensive experiments on the Mayo LDCT dataset demonstrate that the proposed method outperforms existing self-supervised approaches and achieves performance comparable to, or better than, several representative supervised denoising methods.