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 201–225 of 7282 papers

TitleStatusHype
Fast Training of Diffusion Models with Masked TransformersCode2
FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimation via Diffusion ModelsCode2
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial OptimizationCode2
A Simple and Model-Free Path Filtering Algorithm for Smoothing and AccuracyCode2
DiGress: Discrete Denoising diffusion for graph generationCode2
DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup TablesCode2
EAMamba: Efficient All-Around Vision State Space Model for Image RestorationCode2
Diffusion Recommender ModelCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
FreeInit: Bridging Initialization Gap in Video Diffusion ModelsCode2
Exposure Bracketing Is All You Need For A High-Quality ImageCode2
Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory SharpeningCode2
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion ModelsCode2
Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion ModelCode2
Diffusion Predictive Control with ConstraintsCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
Generative AI for Medical Imaging: extending the MONAI FrameworkCode2
DiffusionTrack: Diffusion Model For Multi-Object TrackingCode2
CARD: Classification and Regression Diffusion ModelsCode2
Generative Time Series Forecasting with Diffusion, Denoise, and DisentanglementCode2
CascadedGaze: Efficiency in Global Context Extraction for Image RestorationCode2
Geodesic Diffusion Models for Medical Image-to-Image GenerationCode2
Diffusion models as plug-and-play priorsCode2
Diffusion Models and Representation Learning: A SurveyCode2
Diffusion Models in Vision: A SurveyCode2
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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