arXiv:2512. 23818v2 Announce Type: replace-cross Abstract: Denoising and score estimation are classically linked through Tweedie's formula, which relates the posterior mean under Gaussian noise to the Stein score of the noisy marginal.
By Andrej Leban
The paper investigates learning a generative model for clean images from only noisy observations. It shows that Noise2Noise regression can learn posterior predictions at or above the training noise level, but to predict below that level a consistency constraint is required. The authors derive conditions under which this consistency uniquely determines the continuation to the clean image distribution and validate the approach on CIFAR-10, photon‑counting microscopy, and synthetic aperture radar data.
By Fabian Kruse, Lorenzo Baldassari, Ivan Dokmani\'c
The paper introduces a physics‑driven, cross‑domain iterative framework for self‑supervised low‑dose CT denoising. It first uses a learned sinogram prior and the LDCT noise model to separate Poisson and Gaussian noise components, then applies binomial and Gaussian data thinning to create two training pairs with independent noise realizations. These pairs train an image‑domain network whose outputs are forward‑projected to refine the prior, yielding consistent performance gains over existing self‑supervised baselines and comparable results to supervised methods.
By Xianlei Han, Shaoyu Wang, Jiancheng Fang, Weiwen Wu, Qiegen Liu
The paper proposes using the inherent shot‑noise from quantum measurements as a resource for diffusion models, replacing part of the stochastic noise normally injected during generation. By modeling measurement error as multinomial sampling noise and mapping the diffusion process onto a bounded hyperplane, the authors derive a state‑dependent adaptive shot budget that matches the mean and covariance of a shot‑noise‑free update. Experiments on an eight‑dimensional Gaussian mixture and MNIST latent diffusion demonstrate that this approach achieves comparable generation quality with dramatically fewer shots—down to 303 shots per sample versus 1.6 × 10⁶ in a high‑shot baseline.
By Tianhao Guo, Dianxin Zheng, Yuhua Qian, Yaohui Zheng, Yuzhi Yang, Merouane Debbah, Chau Yuen
arXiv:2606. 03393v1 Announce Type: new Abstract: We propose a novel diffusion model, Flicker-DDPM, which incorporates flicker (1/f) noise inspired by self-organized criticality (SOC), a widely observed phenomenon in natural systems.
By Kexiang Mao
arXiv:2602. 09639v2 Announce Type: replace Abstract: Denoising diffusion models (DDMs) are state-of-the-art methods for learning densities from data across numerous domains, yet many aspects of the training and sampling pipeline remain poorly understood.
By Zahra Kadkhodaie, Aram-Alexandre Pooladian, Sinho Chewi, Eero Simoncelli