SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

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Showing 451–500 of 7282 papers

TitleStatusHype
FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video GenerationCode1
TracLLM: A Generic Framework for Attributing Long Context LLMsCode1
Adaptive Differential Denoising for Respiratory Sounds ClassificationCode1
Unleashing High-Quality Image Generation in Diffusion Sampling Using Second-Order Levenberg-Marquardt-LangevinCode1
ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory ImputationCode1
Minute-Long Videos with Dual ParallelismsCode1
Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective ApproachCode1
Multimodal LLM-Guided Semantic Correction in Text-to-Image DiffusionCode1
Rotation-Equivariant Self-Supervised Method in Image DenoisingCode1
Step-level Reward for Free in RL-based T2I Diffusion Model Fine-tuningCode1
OSCAR: One-Step Diffusion Codec for Image Compression Across Multiple Bit-ratesCode1
REPA Works Until It Doesn't: Early-Stopped, Holistic Alignment Supercharges Diffusion TrainingCode1
Guided Diffusion Sampling on Function Spaces with Applications to PDEsCode1
DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation TrainerCode1
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool UseCode1
FlowPure: Continuous Normalizing Flows for Adversarial PurificationCode1
Video-GPT via Next Clip DiffusionCode1
DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion ModelCode1
Total Variation-Based Image Decomposition and Denoising for Microscopy ImagesCode1
TS-Diff: Two-Stage Diffusion Model for Low-Light RAW Image EnhancementCode1
Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation AbilityCode1
Multi-View Learning with Context-Guided Receptance for Image DenoisingCode1
EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound NoiseCode1
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup FunctionalCode1
Physics-guided and fabrication-aware inverse design of photonic devices using diffusion modelsCode1
Survey of Video Diffusion Models: Foundations, Implementations, and ApplicationsCode1
Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided CalibrationCode1
WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba DiffusionCode1
Filter2Noise: Interpretable Self-Supervised Single-Image Denoising for Low-Dose CT with Attention-Guided Bilateral FilteringCode1
TTRD3: Texture Transfer Residual Denoising Dual Diffusion Model for Remote Sensing Image Super-ResolutionCode1
The Tenth NTIRE 2025 Image Denoising Challenge ReportCode1
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual PerceptionCode1
Invariance Matters: Empowering Social Recommendation via Graph Invariant LearningCode1
ID-Booth: Identity-consistent Face Generation with Diffusion ModelsCode1
MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-ResolutionCode1
Model Reveals What to Cache: Profiling-Based Feature Reuse for Video Diffusion ModelsCode1
Fine-Tuning Visual Autoregressive Models for Subject-Driven GenerationCode1
NoProp: Training Neural Networks without Back-propagation or Forward-propagationCode1
Enhancing Creative Generation on Stable Diffusion-based ModelsCode1
Devil is in the Uniformity: Exploring Diverse Learners within Transformer for Image RestorationCode1
MiLA: Multi-view Intensive-fidelity Long-term Video Generation World Model for Autonomous DrivingCode1
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light ScenariosCode1
DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion GenerationCode1
AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial AttacksCode1
Controlling Latent Diffusion Using Latent CLIPCode1
TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion ModelsCode1
Illuminating Darkness: Enhancing Real-world Low-light Scenes with Smartphone ImagesCode1
One-Step Diffusion Model for Image Motion-DeblurringCode1
A Light and Tuning-free Method for Simulating Camera Motion in Video GenerationCode1
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified