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 1301–1325 of 7282 papers

TitleStatusHype
Accelerating Diffusion Models via Early Stop of the Diffusion ProcessCode1
Online Deep Equilibrium Learning for Regularization by DenoisingCode1
Decoder Denoising Pretraining for Semantic SegmentationCode1
Flexible Diffusion Modeling of Long VideosCode1
A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2NoiseCode1
Masked Image Modeling with Denoising ContrastCode1
Deterministic training of generative autoencoders using invertible layersCode1
RU-Net: Regularized Unrolling Network for Scene Graph GenerationCode1
Subspace Diffusion Generative ModelsCode1
StorSeismic: A new paradigm in deep learning for seismic processingCode1
Adversarial Distortion Learning for Medical Image DenoisingCode1
Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video ParsingCode1
Less is More: Reweighting Important Spectral Graph Features for RecommendationCode1
Gabor is Enough: Interpretable Deep Denoising with a Gabor Synthesis Dictionary PriorCode1
Learn from Unpaired Data for Image Restoration: A Variational Bayes ApproachCode1
Self-supervised Learning for Sonar Image ClassificationCode1
ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak SupervisionCode1
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising PackageCode1
Self-Guided Learning to Denoise for Robust RecommendationCode1
BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial NetworksCode1
Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward OnesCode1
Total Variation Optimization Layers for Computer VisionCode1
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial TrainingCode1
Perception Prioritized Training of Diffusion ModelsCode1
StyleFool: Fooling Video Classification Systems via Style TransferCode1
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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