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 2551–2600 of 7282 papers

TitleStatusHype
Hierarchical Relational Networks for Group Activity Recognition and RetrievalCode0
DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise CleaningCode0
Learning Pixel-Distribution Prior with Wider Convolution for Image DenoisingCode0
Learning Priors in High-frequency Domain for Inverse Imaging ReconstructionCode0
Hiding Images in Diffusion Models by Editing Learned Score FunctionsCode0
Heteroskedastic PCA: Algorithm, Optimality, and ApplicationsCode0
Anatomical Priors for Image Segmentation via Post-Processing with Denoising AutoencodersCode0
H-FND: Hierarchical False-Negative Denoising for Distant Supervision Relation ExtractionCode0
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient NetworkCode0
Image Denoising with Control over Deep Network HallucinationCode0
Learning to Bound: A Generative Cramér-Rao BoundCode0
HandDAGT: A Denoising Adaptive Graph Transformer for 3D Hand Pose EstimationCode0
Haar-Laplacian for directed graphsCode0
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal frameworkCode0
Guided Image Synthesis via Initial Image Editing in Diffusion ModelCode0
ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative TransformerCode0
Guided and Variance-Corrected Fusion with One-shot Style Alignment for Large-Content Image GenerationCode0
A note on the evaluation of generative modelsCode0
Discrete Object Generation with Reversible Inductive ConstructionCode0
CoDiCast: Conditional Diffusion Model for Global Weather Prediction with Uncertainty QuantificationCode0
Grids Often Outperform Implicit Neural RepresentationsCode0
Discrete Denoising Diffusion Approach to Integer FactorizationCode0
Graph topology inference benchmarks for machine learningCode0
Ground Truth Free Denoising by Optimal TransportCode0
HDRUNet: Single Image HDR Reconstruction with Denoising and DequantizationCode0
Graph Denoising with Framelet RegularizerCode0
Dirty Pixels: Towards End-to-End Image Processing and PerceptionCode0
CoDe: Blockwise Control for Denoising Diffusion ModelsCode0
A Review of Convolutional Neural Networks for Inverse Problems in ImagingCode0
Graph Adversarial Diffusion ConvolutionCode0
CocoNet: A deep neural network for mapping pixel coordinates to color valuesCode0
Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray DenoisingCode0
DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-ConsistencyCode0
Global Point Cloud Registration Network for Large TransformationsCode0
Going beyond Compositions, DDPMs Can Produce Zero-Shot InterpolationsCode0
An optimized pipeline for functional connectivity analysis in the rat brainCode0
Global Counterfactual DirectionsCode0
Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise DetectionCode0
Graph Signal Recovery Using Restricted Boltzmann MachinesCode0
End-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional NetworksCode0
Heavy-tailed denoising score matchingCode0
Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamicsCode0
GeoGuide: Geometric guidance of diffusion modelsCode0
GeomCLIP: Contrastive Geometry-Text Pre-training for MoleculesCode0
Geometric-Facilitated Denoising Diffusion Model for 3D Molecule GenerationCode0
CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter PerformanceCode0
GenPlan: Generative Sequence Models as Adaptive PlannersCode0
A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentationCode0
Generative Plug and Play: Posterior Sampling for Inverse ProblemsCode0
Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation StudyCode0
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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