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 4426–4450 of 7282 papers

TitleStatusHype
Incorporating Broad Phonetic Information for Speech Enhancement—0
Incorporating Multi-Target in Multi-Stage Speech Enhancement Model for Better Generalization—0
Increasing Compactness Of Deep Learning Based Speech Enhancement Models With Parameter Pruning And Quantization Techniques—0
InDeed: Interpretable image deep decomposition with guaranteed generalizability—0
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors—0
Independent finite approximations for Bayesian nonparametric inference—0
Independent versus truncated finite approximations for Bayesian nonparametric inference—0
A Diffusion-based Method for Multi-turn Compositional Image Generation—0
The last Dance : Robust backdoor attack via diffusion models and bayesian approach—0
Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions—0
Inference in Graphical Models via Semidefinite Programming Hierarchies—0
Inference of Network Summary Statistics Through Network Denoising—0
Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps—0
Inference-Time Scaling for Flow Models via Stochastic Generation and Rollover Budget Forcing—0
The Latent Road to Atoms: Backmapping Coarse-grained Protein Structures with Latent Diffusion—0
InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training—0
Inferring, Predicting, and Denoising Causal Wave Dynamics—0
InfiniteAudio: Infinite-Length Audio Generation with Consistency—0
Infinite Sparse Structured Factor Analysis—0
Information Maximization via Variational Autoencoders for Cross-Domain Recommendation—0
The LMU Munich Unsupervised Machine Translation Systems—0
Information Theoretic Text-to-Image Alignment—0
Informed Non-convex Robust Principal Component Analysis with Features—0
INFP: Audio-Driven Interactive Head Generation in Dyadic Conversations—0
The LMU Munich Unsupervised Machine Translation System for WMT19—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