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 2251–2300 of 7282 papers

TitleStatusHype
DiffStyler: Diffusion-based Localized Image Style TransferCode1
U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models—0
Global Point Cloud Registration Network for Large TransformationsCode0
DiffGaze: A Diffusion Model for Continuous Gaze Sequence Generation on 360° Images—0
Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion ModelCode2
Boosting Diffusion Models with Moving Average Sampling in Frequency Domain—0
Denoising Table-Text Retrieval for Open-Domain Question AnsweringCode0
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation—0
Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Models—0
GenesisTex: Adapting Image Denoising Diffusion to Texture Space—0
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational AutoencoderCode0
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation—0
Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2—0
Self-Rectifying Diffusion Sampling with Perturbed-Attention GuidanceCode3
Noise2Noise Denoising of CRISM Hyperspectral DataCode1
Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow FieldsCode0
Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image ReconstructionCode1
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image GenerationCode2
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution—0
Iso-Diffusion: Improving Diffusion Probabilistic Models Using the Isotropy of the Additive Gaussian Noise—0
AnimateMe: 4D Facial Expressions via Diffusion Models—0
Multi-Scale Texture Loss for CT denoising with GANsCode0
Make-Your-Anchor: A Diffusion-based 2D Avatar Generation FrameworkCode3
SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation—0
Distilling Semantic Priors from SAM to Efficient Image Restoration Models—0
R3CD: Scene Graph to Image Generation with Relation-aware Compositional Contrastive Control Diffusion—0
Gaze-guided Hand-Object Interaction Synthesis: Dataset and Method—0
Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane—0
FusionINN: Decomposable Image Fusion for Brain Tumor MonitoringCode1
Transfer CLIP for Generalizable Image DenoisingCode2
Ultrasound Imaging based on the Variance of a Diffusion Restoration ModelCode0
TexRO: Generating Delicate Textures of 3D Models by Recursive Optimization—0
DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data—0
Improving Robustness to Model Inversion Attacks via Sparse Coding ArchitecturesCode0
Multimodal-Conditioned Latent Diffusion Models for Fashion Image EditingCode3
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping—0
Powerful Lossy Compression for Noisy Images—0
Analysing Diffusion Segmentation for Medical Images—0
Denoising Diffusion Models for 3D Healthy Brain Tissue InpaintingCode1
AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and ModulationCode3
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly DetectionCode2
Adversary-Robust Graph-Based Learning of WSIs—0
External Knowledge Enhanced 3D Scene Generation from Sketch—0
Physics-Informed Diffusion ModelsCode3
SyncTweedies: A General Generative Framework Based on Synchronized DiffusionsCode2
ReNoise: Real Image Inversion Through Iterative NoisingCode3
Explorative Inbetweening of Time and Space—0
DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models—0
EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive NetworkCode0
DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic ModelCode0
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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