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 851–875 of 7282 papers

TitleStatusHype
Denoising Task Routing for Diffusion ModelsCode1
gDDIM: Generalized denoising diffusion implicit modelsCode1
Adaptive Graph Contrastive Learning for RecommendationCode1
Generalization in diffusion models arises from geometry-adaptive harmonic representationsCode1
Adversarial Schrödinger Bridge MatchingCode1
Denoising Point Clouds in Latent Space via Graph Convolution and Invertible Neural NetworkCode1
3D Vessel Graph Generation Using Denoising DiffusionCode1
Generate What You Prefer: Reshaping Sequential Recommendation via Guided DiffusionCode1
Denoising Relation Extraction from Document-level Distant SupervisionCode1
DenoMamba: A fused state-space model for low-dose CT denoisingCode1
Denoising Multi-Source Weak Supervision for Neural Text ClassificationCode1
Adversarial purification with Score-based generative modelsCode1
Denoising Normalizing FlowCode1
BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder ArchitectureCode1
A Continuous Time Framework for Discrete Denoising ModelsCode1
GENIE: Higher-Order Denoising Diffusion SolversCode1
Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidenceCode1
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation LearningCode1
DialogLM: Pre-trained Model for Long Dialogue Understanding and SummarizationCode1
Beta DiffusionCode1
DiffPortrait3D: Controllable Diffusion for Zero-Shot Portrait View SynthesisCode1
Better Diffusion Models Further Improve Adversarial TrainingCode1
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property PredictionCode1
Graph Bottlenecked Social RecommendationCode1
Diffusion Models for Black-Box OptimizationCode1
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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