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 1301–1350 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
Self-Guided Learning to Denoise for Robust RecommendationCode1
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising PackageCode1
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
Target and Task specific Source-Free Domain Adaptive Image SegmentationCode1
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce SearchCode1
Reverse Engineering of Imperceptible Adversarial Image PerturbationsCode1
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from ImageCode1
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising AutoencodersCode1
AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot NetworkCode1
A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large ShiftCode1
Diffusion Probabilistic Modeling for Video GenerationCode1
P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose EstimationCode1
Representation Learning for Resource-Constrained Keyphrase GenerationCode1
Blind2Unblind: Self-Supervised Image Denoising with Visible Blind SpotsCode1
PD-Flow: A Point Cloud Denoising Framework with Normalizing FlowsCode1
Multi-Scale Adaptive Network for Single Image DenoisingCode1
Diffusion Models for Medical Anomaly DetectionCode1
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image ReconstructionCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
Selective Residual M-Net for Real Image DenoisingCode1
GeoBi-GNN: Geometry-aware Bi-domain Mesh Denoising via Graph Neural NetworksCode1
CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT DenoisingCode1
Conditional Simulation Using Diffusion Schrödinger BridgesCode1
Computing Multiple Image Reconstructions with a Single HypernetworkCode1
Point Cloud Denoising via Momentum Ascent in Gradient FieldsCode1
MANet: Improving Video Denoising with a Multi-Alignment NetworkCode1
C2N: Practical Generative Noise Modeling for Real-World DenoisingCode1
A Two-Stage U-Net for High-Fidelity Denoising of Historical RecordingsCode1
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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