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 2801–2825 of 7282 papers

TitleStatusHype
Spice-E : Structural Priors in 3D Diffusion using Cross-Entity Attention—0
Using Ornstein-Uhlenbeck Process to understand Denoising Diffusion Probabilistic Model and its Noise Schedules—0
SODA: Bottleneck Diffusion Models for Representation LearningCode1
Image Inpainting via Tractable Steering of Diffusion ModelsCode0
Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insightCode0
LC4SV: A Denoising Framework Learning to Compensate for Unseen Speaker Verification Models—0
Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization—0
D4AM: A General Denoising Framework for Downstream Acoustic ModelsCode1
Ranni: Taming Text-to-Image Diffusion for Accurate Instruction FollowingCode5
Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain—0
ReMoS: 3D Motion-Conditioned Reaction Synthesis for Two-Person InteractionsCode1
DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature ReuseCode1
Ultra-short-term multi-step wind speed prediction for wind farms based on adaptive noise reduction technology and temporal convolutional networkCode1
Bayesian Formulations for Graph Spectral Denoising—0
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations—0
TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion ModelsCode1
LFSRDiff: Light Field Image Super-Resolution via Diffusion ModelsCode1
One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls—0
Exploring Attribute Variations in Style-based GANs using Diffusion Models—0
Flow-Guided Diffusion for Video InpaintingCode2
Self-supervised OCT Image Denoising with Slice-to-Slice Registration and ReconstructionCode0
GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction—0
Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual NoiseCode1
DECap: Towards Generalized Explicit Caption Editing via Diffusion Mechanism—0
FreePIH: Training-Free Painterly Image Harmonization with Diffusion Model—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