Diffusion-Based Denoising

Latest papers

51 papers10 papers in the last four weeks, up 150% on the four weeks before. 0.1% of all new papers.

Diffusion Plug-in Priors for Self-Supervised Ultrasound Video Restoration
Nov 3, 2026Chu Chen, Mingduo He, Yining Li +6Video Restoration Ultrasound Imaging
Department of Engineering, University of Cambridge, Cambridge, UK · Department of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China · Department of Intelligent Medical Engineering, School of Medicine, Nankai University, Tianjin, China +7
Denoising Is Stein: Moment Access and a Resource Hierarchy for Learning Hidden Structure in Diffusion Models
Nov 2, 2026Rui WangScore-Based Generative Modeling Diffusion-Based Denoising
Vancouver, BC, Canada
The Noise Plane: A Diffusion Bridge Across Exponent and Dispersion
Oct 27, 2026Xuran Hu, Yuru Jia, Tengxi Wang +3Diffusion-Based Denoising Diffusion Models for Image Restoration
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China · KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden · University of Montenegro, 81000 Podgorica, Montenegro
Shared-Factor-Conditioned Diffusion for Low-Rank Tensor Recovery
Oct 26, 2026Zhengyun Cheng, Guanwen Zhang, Wei ZhouTensor Completion Diffusion-Based Denoising
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China
The Noise-Transmission Burden of Diffusion Network: Why Image Prediction Can Outperform Noise Prediction
Oct 26, 2026Gyeonghun Kang, Pengxi Liu, Bingshuo Qian +1Diffusion-Based Denoising Diffusion Models
Duke University, Durham, NC, USA
ResGVC: Residual Coding for High-fidelity Generative Video Compression
Oct 22, 2026Ding Ding, Daowen Li, Ying Chen +1Generative Video Compression Neural Video Compression
Alibaba Group
How Do DiTs Denoise? An Aggregator-Predictor View of Attention and MLPs
Oct 21, 2026Gyeonghun Kang, William Xingxu Chen, Xiang ChengDiffusion Transformer Diffusion-Based Denoising
Department of Statistical Science, Duke University · Department of Computer Science, Duke University · Department of Electrical and Computer Engineering, Duke University
Seeing through Denoising: Frequency-guided One-step Denoising for Infrared Small Target Detection
Oct 21, 2026Xuelin Qian, Zixuan Wang, Chang Xu +2Infrared Small Target Detection Diffusion Models
Privacy Accounting for Empirical Denoisers
Oct 20, 2026William Cheng, Dongjun Kim, Mijung ParkDifferential Privacy Diffusion Sampling
University of British Columbia · Stanford University · Now at Adobe.
ReState: Noise-Level-Aware State Correction for Training-Free Object Removal in Few-Step Diffusion Models
Oct 15, 2026Donghwan Lee, Sanghyun Jo, Sihyun Kim +2Object Removal Diffusion-Based Image Editing
Seoul National University · OGQ
AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers
Oct 12, 2026Javad Rajabi, Koorosh Roohi, David B. Lindell +1Diffusion Transformer Diffusion-Based Image Generation
University of Toronto · Vector Institute
APEX: Asynchronous Prefix Denoising for Extreme Compression
Oct 9, 2026David Dinkevich, Nisan Chiprut, Yoav HaCohen +1Latent Diffusion Models Diffusion Model Compression
Lightricks · Hebrew University of Jerusalem
PreviewDiff: Multimodal Critic-Guided Search over Diffusion Latents
Sep 28, 2026Vighnesh Subramaniam, Boris Katz, Brian Cheung +3Diffusion Model Guidance T2I Generation
MIT CSAIL · Google Cloud AI Research · UCSF
Unlocking Few-Step Diffusion for Faithful Previews
Sep 28, 2026Jing Jia, Sifan Liu, Guanyang WangDiffusion Sampling Few-Step Diffusion Sampling
Department of Computer Science, Rutgers University · Department of Statistical Science, Duke University · Department of Statistics, Rutgers University
Fill2SR: Repurposing Inpainting Diffusion Transformers for Real-World Super-Resolution
Sep 27, 2026Xingfu Yi, Xiaoxue YuDiffusion Models for Image Restoration Diffusion Transformer
Independent Researcher, Hangzhou, China · Zhejiang University, Hangzhou, China
PixelDiT2: Representation-Grounded Pixel Diffusion Transformers
Sep 21, 2026Yongsheng Yu, Wei Xiong, Yichen Sheng +2Diffusion Model Guidance Diffusion Transformer
NVIDIA · University of Rochester
Denoising Diffusion Generative Models Secretly Calculate Attentions
Sep 1, 2026Farzan Haddadi, Leila Monfared, Ebrahim Rezaii +3Attention Control in Diffusion Models Diffusion-Based Image Generation
School of Electrical Engineering, Iran University of Science & Technology, Tehran, IRAN · Independent researcher
Optimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization
Aug 18, 2026Travis Zhang, Christian Belardi, Justin Lovelace +4Diffusion Timestep Scheduling Few-Step Diffusion Sampling
Cornell University
DualStabSleepNet: A Dual-Domain Diffusion Stabilization Network for Robust Sleep Staging
Aug 17, 2026Chongjian Wang, Chen Liu, Junjie Gao +3Diffusion-Based Denoising Sleep Stage Classification
School of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China · School of Artificial Intelligence, Shandong Women’s University, Jinan, 250352, Shandong, China · School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510641, Guangdong, China
StyleComposer: Training-Free Multi-Reference Style Composition
Aug 5, 2026Sanghyeok Lee, Jihye Kang, Namhyuk AhnImage Style Transfer Compositional T2I Generation
Inha University
Rectify Then Diffuse: Disentangling Concepts Before Denoising Trajectory Unfolds
Aug 4, 2026Ning Zhu, An Chen, Mengfei Zhao +4T2I Generation Attention Control in Diffusion Models
Glasgow College, University of Electronic Science and Technology of China · School of Mathematical Sciences, University of Electronic Science and Technology of China
FeatFix: Reuse What You Verify through Local Exact-Feature Correction for Faster Cached Diffusion Inference
Jul 30, 2026Hanshuai Cui, Zhiqing Tang, Zhi Yao +3Diffusion-Based Image Generation Diffusion Model Caching
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China · Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai 519087, China
FARI: Robust One-Step Inversion for Watermarking in Diffusion Models
Jul 29, 2026Jindong Yang, Han Fang, Weiming Zhang +2Diffusion Models Diffusion Model Watermarking
University of Science and Technology of China · 2Anhui Province Key Laboratory of Digital Security
Noise-Free One-Step LoRA for Task-Driven Image Restoration with Diffusion Priors
Jul 28, 2026Jaeha Kim, Kyoung Mu LeeImage Restoration Task-Driven Image Restoration
Dept. of ECE & ASRI, Seoul National University, Seoul, Korea · IPAI, Seoul National University, Seoul, Korea
ScaleResfusion: Residual Rectified Flow based on Residual Vector Field
Jul 28, 2026Zhenning Shi, Chen Xu, Junhao Zhang +4Diffusion Models for Image Restoration Rectified Flow
College of Computer Science, Nankai University, Tianjin, China · Faculty of Information Science and Computing, University of Macau, Macau, China · CSIRO Data61, Australia +1
Nuclear Quantum Effects as a Denoising Problem
Jul 22, 2026Weizhou Wang, Jonathan Weare, Aaron R. DinnerDiffusion-Based Denoising Quantum Chemistry
Department of Chemistry, University of Chicago, Chicago, Illinois 60637, USA · Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA · James Franck Institute, University of Chicago, Chicago, Illinois 60637, USA
When to Plan, When to Polish: Noise Level as a Granularity Axis for Diffusion Language Models
Jun 19, 2026Peihong Li, Yuanjie Shi, Yan YanLLM Planning Diffusion Language Model Decoding
Washington State University
Show the Signal, Hide the Noise: Spectral Forcing for Pixel-Space Diffusion
Jun 13, 2026Weichen Fan, Haiwen Diao, Penghao Wu +1Pixel-Space Diffusion Models Diffusion-Based Image Generation
1S-Lab, Nanyang Technological University
DuET: Dual Expert Trajectories for Diffusion Image Editing
Jun 11, 2026Lidia Troeshestova, Alexander Ustyuzhanin, Sergey KastryulinText-Guided Image Editing Diffusion-Based Image Editing
HSE University · Yandex
AsyncPatch Diffusion: spatially-flexible image generation
Jun 5, 2026Samuele Papa, Valentin De Bortoli, Guillaume Couairon +3Spatially Grounded Image Generation Diffusion Model Guidance
Google DeepMind · University of Amsterdam · The Netherlands Cancer Institute

Nov 3, 2026J/K move · Enter open · S save

Diffusion Plug-in Priors for Self-Supervised Ultrasound Video Restoration

Chu Chen, Mingduo He, Yining Li, Yinhao Wu+5

Department of Engineering, University of Cambridge, Cambridge, UK · Department of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China · Department of Intelligent Medical Engineering, School of Medicine, Nankai University, Tianjin, China+7

Video Restoration Ultrasound Imaging Video Super-Resolution Diffusion-Based Inverse Problems Diffusion-Based Denoising

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Ultrasound videos are limited by hardware-constrained spatial resolution and signal-dependent speckle, while paired low- and high-resolution acquisitions for supervised restoration are rarely available. We aim to jointly super-resolve and despeckle ultrasound videos without paired training data or network retraining. We propose UltraDiff, which uses a frozen pre-trained diffusion model (DM) as a plug-in prior and restores each frame by optimizing the latent seed of the DM sampler under a range-constrained maximum a posteriori formulation. We derive a speckle-aware data-fidelity term from a Rayleigh scale-family model and characterize its relative-residual and variance-adaptive weighting properties. We further introduce a stabilized fidelity that is bounded below and strictly convex in log intensity for positive observations in its unclamped form. A total-variation regularizer complements the DM prior, and a temporal seed update warm-starts each frame from the optimized seed of its predecessor. On EchoNet-LVH videos with simulated speckle and additive noise, UltraDiff with a natural-image prior improves PSNR by 1.24,dB over the strongest baseline, and ultrasound-related priors improve it further. On real clinical echocardiography videos, UltraDiff reduces the mean absolute error of zero-shot ejection fraction (EF) prediction from 7.31 to 6.37 EF points relative to DUP, the strongest baseline. Combining a speckle-aware fidelity with frozen diffusion priors enables ground-truth-free ultrasound video restoration that benefits downstream measurement. UltraDiff needs neither paired data nor retraining and is not tied to a specific diffusion checkpoint. Code associated with this work will be made publicly available upon acceptance.