# Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data

arXiv: https://arxiv.org/abs/2501.19094 · HTML: https://mlsanity.com/paper/2501.19094/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 31 Jan 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Tackling the Generative Learning Trilemma with Denoising Diffusion GANs](https://mlsanity.com/paper/2112.07804/) (2021)
- [TorchRadon: Fast Differentiable Routines for Computed Tomography](https://mlsanity.com/paper/2009.14788/) (2020)
- [Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model](https://mlsanity.com/paper/2011.07995/) (2020)
- [Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods](https://mlsanity.com/paper/1905.06330/) (2019)
- [Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks](https://mlsanity.com/paper/2106.14324/) (2021)
- [Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods](https://mlsanity.com/paper/2006.00112/) (2020)
- [Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context](https://mlsanity.com/paper/2309.10817/) (2023)
- [Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks](https://mlsanity.com/paper/2304.00433/) (2023)
- [Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data](https://mlsanity.com/paper/2402.01186/) (2024)
- [AmbientCycleGAN for Establishing Interpretable Stochastic Object Models Based on Mathematical Phantoms and Medical Imaging Measurements](https://mlsanity.com/paper/2402.01171/) (2024)

## Built on by (most cited first, 2 of 2)

- [Regression is all you need for medical image translation](https://mlsanity.com/paper/2505.02048/) (2025)
- [Establishing Stochastic Object Models from Noisy Data via Ambient Measurement-Integrated Diffusion](https://mlsanity.com/paper/2512.14187/) (2025)

## Abstract (arXiv)

It is widely accepted that medical imaging systems should be objectively assessed via task-based image quality (IQ) measures that ideally account for all sources of randomness in the measured image data, including the variation in the ensemble of objects to be imaged. Stochastic object models (SOMs) that can randomly draw samples from the object distribution can be employed to characterize object variability. To establish realistic SOMs for task-based IQ analysis, it is desirable to employ experimental image data. However, experimental image data acquired from medical imaging systems are subject to measurement noise. Previous work investigated the ability of deep generative models (DGMs) that employ an augmented generative adversarial network (GAN), AmbientGAN, for establishing SOMs from noisy measured image data. Recently, denoising diffusion models (DDMs) have emerged as a leading DGM for image synthesis and can produce superior image quality than GANs. However, original DDMs possess a slow image-generation process because of the Gaussian assumption in the denoising steps. More recently, denoising diffusion GAN (DDGAN) was proposed to permit fast image generation while maintain high generated image quality that is comparable to the original DDMs. In this work, we propose an augmented DDGAN architecture, Ambient DDGAN (ADDGAN), for learning SOMs from noisy image data. Numerical studies that consider clinical computed tomography (CT) images and digital breast tomosynthesis (DBT) images are conducted. The ability of the proposed ADDGAN to learn realistic SOMs from noisy image data is demonstrated. It has been shown that the ADDGAN significantly outperforms the advanced AmbientGAN models for synthesizing high resolution medical images with complex textures.