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 3876–3900 of 7282 papers

TitleStatusHype
DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention—0
DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model—0
Multiple-Question Multiple-Answer Text-VQA—0
Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models—0
Cross-domain feature disentanglement for interpretable modeling of tumor microenvironment impact on drug response—0
UT5: Pretraining Non autoregressive T5 with unrolled denoising—0
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and DecodingCode0
Brain-Driven Representation Learning Based on Diffusion Model—0
Near-Field Sparse Channel Estimation for Extremely Large-Scale RIS-Aided Wireless Communications—0
AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising—0
Robust semi-supervised segmentation with timestep ensembling diffusion models—0
One Signal-Noise Separation based Wiener Filter for Magnetogastrogram—0
Physics-Informed Data Denoising for Real-Life Sensing Systems—0
Back to Basics: Fast Denoising Iterative Algorithm—0
ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints—0
Diffusion-Generative Multi-Fidelity Learning for Physical Simulation—0
L-WaveBlock: A Novel Feature Extractor Leveraging Wavelets for Generative Adversarial Networks—0
Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation—0
Improved DDIM Sampling with Moment Matching Gaussian Mixtures—0
LuminanceL1Loss: A loss function which measures percieved brightness and colour differences—0
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising ApproachCode0
DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET ImagingCode0
3DifFusionDet: Diffusion Model for 3D Object Detection with Robust LiDAR-Camera Fusion—0
Reducing Spatial Fitting Error in Distillation of Denoising Diffusion ModelsCode0
Zero-Shot Enhancement of Low-Light Image Based on Retinex DecompositionCode0
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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