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

Showing 751775 of 7282 papers

TitleStatusHype
Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion ModelsCode1
Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality AssessmentCode1
SRNDiff: Short-term Rainfall Nowcasting with Condition Diffusion ModelCode1
DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging LoadCode1
Stochastic Localization via Iterative Posterior SamplingCode1
Explaining generative diffusion models via visual analysis for interpretable decision-making processCode1
Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion ModelCode1
Color Image Denoising Using The Green Channel PriorCode1
Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy BiasesCode1
Particle Denoising Diffusion SamplerCode1
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation LearningCode1
Social Physics Informed Diffusion Model for Crowd SimulationCode1
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance SegmentationCode1
Unified Discrete Diffusion for Categorical DataCode1
SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal DenoisingCode1
Pard: Permutation-Invariant Autoregressive Diffusion for Graph GenerationCode1
ViewFusion: Learning Composable Diffusion Models for Novel View SynthesisCode1
Diffusive Gibbs SamplingCode1
LIR: A Lightweight Baseline for Image RestorationCode1
Plug-and-Play image restoration with Stochastic deNOising REgularizationCode1
Diffusion-based Graph Generative MethodsCode1
Masked Pre-training Enables Universal Zero-shot DenoiserCode1
Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle RecognitionCode1
Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet ExtractionCode1
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified