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 251–275 of 7282 papers

TitleStatusHype
3D Human Mesh Estimation from Virtual MarkersCode2
MB-TaylorFormer V2: Improved Multi-branch Linear Transformer Expanded by Taylor Formula for Image RestorationCode2
BAMM: Bidirectional Autoregressive Motion ModelCode2
Medical Vision Generalist: Unifying Medical Imaging Tasks in ContextCode2
DiSA: Diffusion Step Annealing in Autoregressive Image GenerationCode2
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial OptimizationCode2
Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache SharingCode2
DiGress: Discrete Denoising diffusion for graph generationCode2
Discrete Diffusion Modeling by Estimating the Ratios of the Data DistributionCode2
DPoser: Diffusion Model as Robust 3D Human Pose PriorCode2
EchoScene: Indoor Scene Generation via Information Echo over Scene Graph DiffusionCode2
FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive LearningCode2
Aligning Text-to-Image Diffusion Models with Reward BackpropagationCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
Diffusion Recommender ModelCode2
Diffusion Predictive Control with ConstraintsCode2
Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion ModelCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory SharpeningCode2
All-In-One Medical Image Restoration via Task-Adaptive RoutingCode2
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelCode2
Diffusion models as plug-and-play priorsCode2
Diffusion Models in Vision: A SurveyCode2
DiffusionTrack: Diffusion Model For Multi-Object TrackingCode2
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
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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