# Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging

arXiv: https://arxiv.org/abs/2106.12175 · HTML: https://mlsanity.com/paper/2106.12175/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [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)
- [Low-dose CT denoising with convolutional neural network](https://mlsanity.com/paper/1610.00321/) (2016)
- [200x Low-dose PET Reconstruction using Deep Learning](https://mlsanity.com/paper/1712.04119/) (2017)
- [Temporal and volumetric denoising via quantile sparse image prior](https://mlsanity.com/paper/1802.03943/) (2018)

## Built on by (most cited first, 15 of 15)

- [Deformation-Compensated Learning for Image Reconstruction without Ground Truth](https://mlsanity.com/paper/2107.05533/) (2021)
- [Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain](https://mlsanity.com/paper/2402.06190/) (2024)
- [Neighboring Slice Noise2Noise: Self-Supervised Medical Image Denoising from Single Noisy Image Volume](https://mlsanity.com/paper/2411.10831/) (2024)
- [DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models](https://mlsanity.com/paper/2504.00264/) (2025)
- [Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image](https://mlsanity.com/paper/2206.01856/) (2022)
- [Perceive and Calibrate: Analyzing and Enhancing Robustness of Medical Multi-Modal Large Language Models](https://mlsanity.com/paper/2512.21964/) (2025)
- [MixCL: Pixel label matters to contrastive learning](https://mlsanity.com/paper/2203.02114/) (2022)
- [Self-supervised Physics-based Denoising for Computed Tomography](https://mlsanity.com/paper/2211.00745/) (2022)
- [Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging Noise](https://mlsanity.com/paper/2310.07887/) (2023)
- [Self-supervised training of deep denoisers in multi-coil MRI considering noise correlations](https://mlsanity.com/paper/2208.07552/) (2022)
- [Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and Tasks](https://mlsanity.com/paper/2209.12157/) (2022)
- [XVertNet: Unsupervised Contrast Enhancement of Vertebral Structures with Dynamic Self-Tuning Guidance and Multi-Stage Analysis](https://mlsanity.com/paper/2306.03983/) (2023)
- [Investigating Self-Supervised Image Denoising with Denaturation](https://mlsanity.com/paper/2405.01124/) (2024)
- [RelativeFlow: Taming Medical Image Denoising Learning with Noisy Reference](https://mlsanity.com/paper/2604.15459/) (2026)
- [NGPS: Structure-Preserving Self-Supervised Denoising via Neighbor-Guided Patch Sampling](https://mlsanity.com/paper/2606.23200/) (2026)

## Abstract (arXiv)

Image denoising is of great importance for medical imaging system, since it can improve image quality for disease diagnosis and downstream image analyses. In a variety of applications, dynamic imaging techniques are utilized to capture the time-varying features of the subject, where multiple images are acquired for the same subject at different time points. Although signal-to-noise ratio of each time frame is usually limited by the short acquisition time, the correlation among different time frames can be exploited to improve denoising results with shared information across time frames. With the success of neural networks in computer vision, supervised deep learning methods show prominent performance in single-image denoising, which rely on large datasets with clean-vs-noisy image pairs. Recently, several self-supervised deep denoising models have been proposed, achieving promising results without needing the pairwise ground truth of clean images. In the field of multi-image denoising, however, very few works have been done on extracting correlated information from multiple slices for denoising using self-supervised deep learning methods. In this work, we propose Deformed2Self, an end-to-end self-supervised deep learning framework for dynamic imaging denoising. It combines single-image and multi-image denoising to improve image quality and use a spatial transformer network to model motion between different slices. Further, it only requires a single noisy image with a few auxiliary observations at different time frames for training and inference. Evaluations on phantom and in vivo data with different noise statistics show that our method has comparable performance to other state-of-the-art unsupervised or self-supervised denoising methods and outperforms under high noise levels.