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 4401–4425 of 7282 papers

TitleStatusHype
What cleaves? Is proteasomal cleavage prediction reaching a ceiling?—0
UETfishes at MEDIQA 2021: Standing-on-the-Shoulders-of-Giants Model for Abstractive Multi-answer Summarization—0
ACT-Net: Anchor-context Action Detection in Surgery Videos—0
Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion—0
Outlier-aware Tensor Robust Principal Component Analysis with Self-guided Data Augmentation—0
Nonlocal Patches based Gaussian Mixture Model for Image Inpainting—0
Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancement—0
Uformer: A UNet-Transformer fused robust end-to-end deep learning framework for real-time denoising of lung sounds—0
P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising—0
PACO: Global Signal Restoration via PAtch COnsensus—0
Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Models—0
Paired Wasserstein Autoencoders for Conditional Sampling—0
Active Tuning—0
UGPNet: Universal Generative Prior for Image Restoration—0
PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
What Regularized Auto-Encoders Learn from the Data Generating Distribution—0
PANDORA: Diffusion Policy Learning for Dexterous Robotic Piano Playing—0
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation—0
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion—0
PanoWan: Lifting Diffusion Video Generation Models to 360° with Latitude/Longitude-aware Mechanisms—0
PARADISE”:" Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining—0
What's the score? Automated Denoising Score Matching for Nonlinear Diffusions—0
Parameter Learning for Log-supermodular Distributions—0
Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR)—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