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 4126–4150 of 7282 papers

TitleStatusHype
Neural Knitworks: Patched Neural Implicit Representation Networks—0
NeuralLift-360: Lifting an In-the-Wild 2D Photo to a 3D Object With 360deg Views—0
Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments—0
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization—0
Neural Prior for Trajectory Estimation—0
Neural shrinkage for wavelet-based SAR despeckling—0
Neural Text Style Transfer via Denoising and Reranking—0
TransDiffuser: End-to-end Trajectory Generation with Decorrelated Multi-modal Representation for Autonomous Driving—0
Transductive Adaptation of Black Box Predictions—0
Neural Universal Discrete Denoiser—0
NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids—0
Evolutionary training-free guidance in diffusion model for 3D multi-objective molecular generation—0
Neuromorphic Imaging with Joint Image Deblurring and Event Denoising—0
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0—0
Transfer learning for self-supervised, blind-spot seismic denoising—0
New Computational Techniques for a Faster Variation of BM3D Image Denoising—0
New explicit thresholding/shrinkage formulas for one class of regularization problems with overlapping group sparsity and their applications—0
New Risk Bounds for 2D Total Variation Denoising—0
NFCNN: Toward a Noise Fusion Convolutional Neural Network for Image Denoising—0
NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training—0
NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generation—0
NL2pSQL: Generating Pseudo-SQL Queries from Under-Specified Natural Language Questions—0
NLP Sampling: Combining MCMC and NLP Methods for Diverse Constrained Sampling—0
Transfer Learning with Label Noise—0
NMR Spectra Denoising with Vandermonde Constraints—0
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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