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 1326–1350 of 7282 papers

TitleStatusHype
DENOASR: Debiasing ASRs through Selective Denoising—0
MBD: Multi b-value Denoising of Diffusion Magnetic Resonance Images—0
TopoDiffusionNet: A Topology-aware Diffusion ModelCode1
DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization—0
Multi-head Sequence Tagging Model for Grammatical Error CorrectionCode0
Diffusion Transformer PolicyCode2
Traffic Matrix Estimation based on Denoising Diffusion Probabilistic ModelCode0
SeaDAG: Semi-autoregressive Diffusion for Conditional Directed Acyclic Graph Generation—0
FastSTI: A Fast Conditional Pseudo Numerical Diffusion Model for Spatio-temporal Traffic Data Imputation—0
IKDP: Inverse Kinematics through Diffusion Process—0
Data Augmentation via Diffusion Model to Enhance AI Fairness—0
MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications—0
On the Wasserstein Convergence and Straightness of Rectified FlowCode0
Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization—0
DEL-Ranking: Ranking-Correction Denoising Framework for Elucidating Molecular Affinities in DNA-Encoded Libraries—0
Truncated Consistency Models—0
Heavy-Tailed Diffusion Models—0
RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training—0
SGD Jittering: A Training Strategy for Robust and Accurate Model-Based Architectures—0
Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers—0
Multi-modal Pose Diffuser: A Multimodal Generative Conditional Pose Prior—0
Dynamic Negative Guidance of Diffusion ModelsCode0
Islanding Detection for Active Distribution Networks Using WaveNet+UNet Classifier—0
Latent Weight Diffusion: Generating reactive policies instead of trajectories—0
Enhancing Crowdsourced Audio for Text-to-Speech Models—0
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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