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 401–450 of 7282 papers

TitleStatusHype
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and GenerationCode2
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent SpacesCode2
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image GenerationCode2
LION: Latent Point Diffusion Models for 3D Shape GenerationCode2
What the DAAM: Interpreting Stable Diffusion Using Cross AttentionCode2
TabDDPM: Modelling Tabular Data with Diffusion ModelsCode2
Protein structure generation via folding diffusionCode2
DiGress: Discrete Denoising diffusion for graph generationCode2
CMGAN: Conformer-Based Metric-GAN for Monaural Speech EnhancementCode2
Diffusion Models in Vision: A SurveyCode2
MotionDiffuse: Text-Driven Human Motion Generation with Diffusion ModelCode2
Augraphy: A Data Augmentation Library for Document ImagesCode2
FRA-RIR: Fast Random Approximation of the Image-source MethodCode2
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelCode2
Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion ModelsCode2
Pose-NDF: Modeling Human Pose Manifolds with Neural Distance FieldsCode2
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex NoiseCode2
Semantic Image Synthesis via Diffusion ModelsCode2
DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change DetectionCode2
Diffusion models as plug-and-play priorsCode2
CARD: Classification and Regression Diffusion ModelsCode2
Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and DenoisingCode2
Improved Vector Quantized Diffusion ModelsCode2
MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and InterpolationCode2
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech SynthesisCode2
On the Generalization of BasicVSR++ to Video Deblurring and DenoisingCode2
iSDF: Real-Time Neural Signed Distance Fields for Robot PerceptionCode2
Practical Blind Image Denoising via Swin-Conv-UNet and Data SynthesisCode2
SUNet: Swin Transformer UNet for Image DenoisingCode2
Speech Denoising in the Waveform Domain with Self-AttentionCode2
Denoising Diffusion Restoration ModelsCode2
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-TrainingCode2
PromptBERT: Improving BERT Sentence Embeddings with PromptsCode2
Improving Image Restoration by Revisiting Global Information AggregationCode2
ExT5: Towards Extreme Multi-Task Scaling for Transfer LearningCode2
Palette: Image-to-Image Diffusion ModelsCode2
QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and NormalizationCode2
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential EquationsCode2
Structured Denoising Diffusion Models in Discrete State-SpacesCode2
SeaD: End-to-end Text-to-SQL Generation with Schema-aware DenoisingCode2
Liquid Warping GAN with Attention: A Unified Framework for Human Image SynthesisCode2
Denoising Diffusion Implicit ModelsCode2
Denoising Diffusion Probabilistic ModelsCode2
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language ModelsCode1
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion ModelsCode1
Elucidated Rolling Diffusion Models for Probabilistic Weather ForecastingCode1
Visual-Instructed Degradation Diffusion for All-in-One Image RestorationCode1
DiffO: Single-step Diffusion for Image Compression at Ultra-Low BitratesCode1
Noise Conditional Variational Score DistillationCode1
StableMTL: Repurposing Latent Diffusion Models for Multi-Task Learning from Partially Annotated Synthetic DatasetsCode1
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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