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 301–325 of 7282 papers

TitleStatusHype
Auto Tensor Singular Value Thresholding: A Non-Iterative and Rank-Free Framework for Tensor Denoising—0
Automated Learning of Semantic Embedding Representations for Diffusion Models—0
Score-based Self-supervised MRI Denoising—0
Denoising Diffusion Probabilistic Models for Coastal Inundation Forecasting—0
Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution LearningCode0
Inter-Diffusion Generation Model of Speakers and Listeners for Effective Communication—0
DiffusionSfM: Predicting Structure and Motion via Ray Origin and Endpoint Diffusion—0
Flow-GRPO: Training Flow Matching Models via Online RLCode7
MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising—0
EDmamba: A Simple yet Effective Event Denoising Method with State Space Model—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
FEMSN: Frequency-Enhanced Multiscale Network for fault diagnosis of rotating machinery under strong noise environments—0
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI ReconstructionCode0
Riemannian Denoising Diffusion Probabilistic Models—0
CountDiffusion: Text-to-Image Synthesis with Training-Free Counting-Guidance Diffusion—0
Spectral and Temporal Denoising for Differentially Private Optimization—0
Non-stationary Diffusion For Probabilistic Time Series ForecastingCode2
TS-Diff: Two-Stage Diffusion Model for Low-Light RAW Image EnhancementCode1
Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Autospeculation—0
Knowledge Distillation for Speech Denoising by Latent Representation Alignment with Cosine Distance—0
Plug-and-Play AMC: Context Is King in Training-Free, Open-Set Modulation with LLMsCode0
Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients—0
Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control—0
Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation AbilityCode1
Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T TrackingCode0
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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