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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6701–6725 of 7282 papers

TitleStatusHype
White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography ImagingCode0
Constrained and unconstrained deep image prior optimization models with automatic regularizationCode0
Guided Image Synthesis via Initial Image Editing in Diffusion ModelCode0
DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-ConsistencyCode0
Semi-Implicit Variational Inference via Score MatchingCode0
UnDIVE: Generalized Underwater Video Enhancement Using Generative PriorsCode0
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
CoDe: Blockwise Control for Denoising Diffusion ModelsCode0
Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-TrainingCode0
Haar-Laplacian for directed graphsCode0
Dirty Pixels: Towards End-to-End Image Processing and PerceptionCode0
Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray DenoisingCode0
D2Diff : A Dual Domain Diffusion Model for Accurate Multi-Contrast MRI SynthesisCode0
Attend to Not Attended: Structure-then-Detail Token Merging for Post-training DiT AccelerationCode0
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal frameworkCode0
Semi-supervised representation learning via dual autoencoders for domain adaptationCode0
Discrete Denoising Diffusion Approach to Integer FactorizationCode0
HandDAGT: A Denoising Adaptive Graph Transformer for 3D Hand Pose EstimationCode0
prDeep: Robust Phase Retrieval with a Flexible Deep NetworkCode0
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual NetworkCode0
Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life PredictionCode0
Discrete Object Generation with Reversible Inductive ConstructionCode0
Leveraging Self-supervised Denoising for Image SegmentationCode0
Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising AutoencodersCode0
Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly DetectionCode0
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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