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 276–300 of 7282 papers

TitleStatusHype
DiSA: Diffusion Step Annealing in Autoregressive Image GenerationCode2
Augraphy: A Data Augmentation Library for Document ImagesCode2
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial OptimizationCode2
dKV-Cache: The Cache for Diffusion Language ModelsCode2
DiffusionTrack: Diffusion Model For Multi-Object TrackingCode2
Diffusion Recommender ModelCode2
Diffusion Transformer PolicyCode2
Diffusion Models in Vision: A SurveyCode2
A Simple and Model-Free Path Filtering Algorithm for Smoothing and AccuracyCode2
Diffusion models as plug-and-play priorsCode2
Diffusion Predictive Control with ConstraintsCode2
EAMamba: Efficient All-Around Vision State Space Model for Image RestorationCode2
DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion ModelsCode2
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
DiffusionInst: Diffusion Model for Instance SegmentationCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
DiffusionBERT: Improving Generative Masked Language Models with Diffusion ModelsCode2
Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory SharpeningCode2
Diffusion Bridge Implicit ModelsCode2
Diffusion-based Generation, Optimization, and Planning in 3D ScenesCode2
DiGress: Discrete Denoising diffusion for graph generationCode2
Anomaly Detection with Conditioned Denoising Diffusion ModelsCode2
Diffusion-based Visual Anagram as Multi-task LearningCode2
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth EstimationCode2
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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