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

Showing 101125 of 7282 papers

TitleStatusHype
3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D GenerationCode3
FreeU: Free Lunch in Diffusion U-NetCode3
Reservoir History Matching of the Norne field with generative exotic priors and a coupled Mixture of Experts -- Physics Informed Neural Operator Forward ModelCode3
RoHM: Robust Human Motion Reconstruction via DiffusionCode3
Scaling Diffusion Transformers to 16 Billion ParametersCode3
Score-Guided Diffusion for 3D Human RecoveryCode3
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous DrivingCode3
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
Diffusion-TS: Interpretable Diffusion for General Time Series GenerationCode3
Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal ConsistencyCode3
DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge DetectionCode3
Taming Stable Diffusion for Text to 360° Panorama Image GenerationCode3
3D Diffuser Actor: Policy Diffusion with 3D Scene RepresentationsCode3
PixelHacker: Image Inpainting with Structural and Semantic ConsistencyCode3
3D Diffuser Actor: Policy Diffusion with 3D Scene RepresentationsCode3
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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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified