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

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Showing 3501–3525 of 7282 papers

TitleStatusHype
Multi-Scale Texture Loss for CT denoising with GANsCode0
Distilling Semantic Priors from SAM to Efficient Image Restoration Models—0
Iso-Diffusion: Improving Diffusion Probabilistic Models Using the Isotropy of the Additive Gaussian Noise—0
AnimateMe: 4D Facial Expressions via Diffusion Models—0
Gaze-guided Hand-Object Interaction Synthesis: Dataset and Method—0
Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane—0
R3CD: Scene Graph to Image Generation with Relation-aware Compositional Contrastive Control Diffusion—0
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
Analysing Diffusion Segmentation for Medical Images—0
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping—0
External Knowledge Enhanced 3D Scene Generation from Sketch—0
Explorative Inbetweening of Time and Space—0
Adversary-Robust Graph-Based Learning of WSIs—0
Powerful Lossy Compression for Noisy Images—0
Improving Robustness to Model Inversion Attacks via Sparse Coding ArchitecturesCode0
DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models—0
DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic ModelCode0
IIDM: Image-to-Image Diffusion Model for Semantic Image SynthesisCode0
EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive NetworkCode0
Depth-guided NeRF Training via Earth Mover's Distance—0
Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising—0
Eigen-CNN: Eigenimages Plus Eigennoise Level Maps Guided Network for Hyperspectral Image DenoisingCode0
WaveFace: Authentic Face Restoration with Efficient Frequency Recovery—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