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 851–900 of 7282 papers

TitleStatusHype
ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow PredictionCode1
Argmax Flows and Multinomial Diffusion: Learning Categorical DistributionsCode1
DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion ModelsCode1
DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion ModelCode1
An Analysis and Mitigation of the Reversal CurseCode1
Adversarial score matching and improved sampling for image generationCode1
Generative Prompt Model for Weakly Supervised Object LocalizationCode1
Generative Proxemics: A Prior for 3D Social Interaction from ImagesCode1
DiffAR: Denoising Diffusion Autoregressive Model for Raw Speech Waveform GenerationCode1
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
GeoBi-GNN: Geometry-aware Bi-domain Mesh Denoising via Graph Neural NetworksCode1
Adversarial Schrödinger Bridge MatchingCode1
DialogLM: Pre-trained Model for Long Dialogue Understanding and SummarizationCode1
DFormer: Diffusion-guided Transformer for Universal Image SegmentationCode1
3D Vessel Graph Generation Using Denoising DiffusionCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property PredictionCode1
Graph Bottlenecked Social RecommendationCode1
Graph Denoising Diffusion for Inverse Protein FoldingCode1
Beta DiffusionCode1
DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein TracesCode1
Better Diffusion Models Further Improve Adversarial TrainingCode1
G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo LabellingCode1
G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo LabellingCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
Guided Diffusion Model for Adversarial PurificationCode1
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIsCode1
HarmoniCa: Harmonizing Training and Inference for Better Feature Caching in Diffusion Transformer AccelerationCode1
Adversarial purification with Score-based generative modelsCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
A Mixture-Based Framework for Guiding Diffusion ModelsCode1
Beyond Image Prior: Embedding Noise Prior into Conditional Denoising TransformerCode1
Designing and Training of A Dual CNN for Image DenoisingCode1
A Continuous Time Framework for Discrete Denoising ModelsCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
Devil is in the Uniformity: Exploring Diverse Learners within Transformer for Image RestorationCode1
Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric VideosCode1
HiPrompt: Tuning-free Higher-Resolution Generation with Hierarchical MLLM PromptsCode1
Beyond Surface Statistics: Scene Representations in a Latent Diffusion ModelCode1
Diffusion Posterior Proximal Sampling for Image RestorationCode1
Efficient Diffusion Training via Min-SNR Weighting StrategyCode1
Homotopic Gradients of Generative Density Priors for MR Image ReconstructionCode1
How Framelets Enhance Graph Neural NetworksCode1
Denoising Relation Extraction from Document-level Distant SupervisionCode1
Denoising Point Clouds in Latent Space via Graph Convolution and Invertible Neural NetworkCode1
Can LLMs be Good Graph Judge for Knowledge Graph Construction?Code1
Hybrid Spectral Denoising Transformer with Guided AttentionCode1
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising PackageCode1
Hyperspectral Image Denoising Using SURE-Based Unsupervised Convolutional Neural NetworksCode1
Denoising Task Routing for Diffusion ModelsCode1
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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