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 101–125 of 7282 papers

TitleStatusHype
Improved Denoising Diffusion Probabilistic ModelsCode3
Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force FieldsCode3
Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal ConsistencyCode3
FreeU: Free Lunch in Diffusion U-NetCode3
Scaling Diffusion Models to Real-World 3D LiDAR Scene CompletionCode3
Scaling Diffusion Transformers to 16 Billion ParametersCode3
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous DrivingCode3
Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted ConceptsCode3
AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and ModulationCode3
DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic ModelsCode3
Discrete Diffusion in Large Language and Multimodal Models: A SurveyCode3
Director3D: Real-world Camera Trajectory and 3D Scene Generation from TextCode3
dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive CachingCode3
Attention Distillation: A Unified Approach to Visual Characteristics TransferCode3
AnyTop: Character Animation Diffusion with Any TopologyCode3
AP-LDM: Attentive and Progressive Latent Diffusion Model for Training-Free High-Resolution Image GenerationCode3
3D Diffuser Actor: Policy Diffusion with 3D Scene RepresentationsCode3
UnMarker: A Universal Attack on Defensive Image WatermarkingCode3
Diffusion-TS: Interpretable Diffusion for General Time Series GenerationCode3
3D Human Mesh Estimation from Virtual MarkersCode2
Diffusion Models in Vision: A SurveyCode2
Diffusion Predictive Control with ConstraintsCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral ConstraintsCode2
Diffusion models as plug-and-play priorsCode2
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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