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 1951–2000 of 7282 papers

TitleStatusHype
LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude EstimationCode0
Leveraging Self-supervised Denoising for Image SegmentationCode0
MaskMedPaint: Masked Medical Image Inpainting with Diffusion Models for Mitigation of Spurious CorrelationsCode0
LED: A Large-scale Real-world Paired Dataset for Event Camera DenoisingCode0
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds DefenseCode0
Learning to Separate Object Sounds by Watching Unlabeled VideoCode0
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentationCode0
BrainCodec: Neural fMRI codec for the decoding of cognitive brain statesCode0
Learning with Noisy Labels by Adaptive Gradient-Based Outlier RemovalCode0
Learning to Kindle the StarlightCode0
Defending Observation Attacks in Deep Reinforcement Learning via Detection and DenoisingCode0
Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2024Code0
Learning to Reach Goals via DiffusionCode0
Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising AutoencodersCode0
Learning to Generate Samples from Noise through Infusion TrainingCode0
Detecting Patch Adversarial Attacks with Image ResidualsCode0
Bayes-optimal learning of an extensive-width neural network from quadratically many samplesCode0
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
Learning to Decouple and Generate Seismic Random Noise via Invertible Neural NetworkCode0
Learning to compress and search visual data in large-scale systemsCode0
Learning to Denoise Distantly-Labeled Data for Entity TypingCode0
Learning to Bound: A Generative Cramér-Rao BoundCode0
Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion ModelsCode0
Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence ForecastingCode0
Learning the optimal Tikhonov regularizer for inverse problemsCode0
Learning to Assimilate in Chaotic Dynamical SystemsCode0
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction PredictionCode0
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving AugmentationCode0
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging ProblemsCode0
Learning Robust 3D Representation from CLIP via Dual DenoisingCode0
Learning parametric dictionaries for graph signalsCode0
Deep Sparse and Low-Rank Prior for Hyperspectral Image DenoisingCode0
Learning Pixel-Distribution Prior with Wider Convolution for Image DenoisingCode0
Learning normalized image densities via dual score matchingCode0
Deep sound-field denoiser: optically-measured sound-field denoising using deep neural networkCode0
Learning of Patch-Based Smooth-Plus-Sparse Models for Image ReconstructionCode0
Learning Priors in High-frequency Domain for Inverse Imaging ReconstructionCode0
Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning ApproachCode0
Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray DenoisingCode0
DeepSign: Deep Learning for Automatic Malware Signature Generation and ClassificationCode0
Learning Instance-Specific Parameters of Black-Box Models Using Differentiable SurrogatesCode0
DeepSat - A Learning framework for Satellite ImageryCode0
(Almost) Smooth Sailing: Towards Numerical Stability of Neural Networks Through Differentiable Regularization of the Condition NumberCode0
Learning in Deep Factor Graphs with Gaussian Belief PropagationCode0
Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise DatasetCode0
Deep Retinex Decomposition for Low-Light EnhancementCode0
CURL: Neural Curve Layers for Global Image EnhancementCode0
BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial NetworkCode0
Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary LearningCode0
Adaptive Mixing of Auxiliary Losses in Supervised LearningCode0
Show:102550
← PrevPage 40 of 146Next →
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