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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 176–200 of 7282 papers

TitleStatusHype
Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action PolicyCode2
Dreamer XL: Towards High-Resolution Text-to-3D Generation via Trajectory Score MatchingCode2
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial OptimizationCode2
DiGress: Discrete Denoising diffusion for graph generationCode2
Diffusion Transformer PolicyCode2
A Simple and Model-Free Path Filtering Algorithm for Smoothing and AccuracyCode2
Diffusion Recommender ModelCode2
Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory SharpeningCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
Augraphy: A Data Augmentation Library for Document ImagesCode2
Discrete Diffusion Modeling by Estimating the Ratios of the Data DistributionCode2
dKV-Cache: The Cache for Diffusion Language ModelsCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
DocDiff: Document Enhancement via Residual Diffusion ModelsCode2
DPoser: Diffusion Model as Robust 3D Human Pose PriorCode2
DiffusionTrack: Diffusion Model For Multi-Object TrackingCode2
DualDn: Dual-domain Denoising via Differentiable ISPCode2
Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object DetectionCode2
Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image RestorationCode2
DiSA: Diffusion Step Annealing in Autoregressive Image GenerationCode2
EchoScene: Indoor Scene Generation via Information Echo over Scene Graph DiffusionCode2
EDICT: Exact Diffusion Inversion via Coupled TransformationsCode2
Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design SpaceCode2
Diffusion models as plug-and-play priorsCode2
Diffusion Models and Representation Learning: 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