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 2276–2300 of 7282 papers

TitleStatusHype
R3CD: Scene Graph to Image Generation with Relation-aware Compositional Contrastive Control Diffusion—0
Gaze-guided Hand-Object Interaction Synthesis: Dataset and Method—0
Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane—0
FusionINN: Decomposable Image Fusion for Brain Tumor MonitoringCode1
Transfer CLIP for Generalizable Image DenoisingCode2
Ultrasound Imaging based on the Variance of a Diffusion Restoration ModelCode0
TexRO: Generating Delicate Textures of 3D Models by Recursive Optimization—0
DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data—0
Improving Robustness to Model Inversion Attacks via Sparse Coding ArchitecturesCode0
Multimodal-Conditioned Latent Diffusion Models for Fashion Image EditingCode3
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping—0
Powerful Lossy Compression for Noisy Images—0
Analysing Diffusion Segmentation for Medical Images—0
Denoising Diffusion Models for 3D Healthy Brain Tissue InpaintingCode1
AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and ModulationCode3
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly DetectionCode2
Adversary-Robust Graph-Based Learning of WSIs—0
External Knowledge Enhanced 3D Scene Generation from Sketch—0
Physics-Informed Diffusion ModelsCode3
SyncTweedies: A General Generative Framework Based on Synchronized DiffusionsCode2
ReNoise: Real Image Inversion Through Iterative NoisingCode3
Explorative Inbetweening of Time and Space—0
DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models—0
EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive NetworkCode0
DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic ModelCode0
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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