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

Showing 71017125 of 7282 papers

TitleStatusHype
Controllable Motion Generation via Diffusion Modal CouplingCode0
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph GenerationCode0
Averaged Deep Denoisers for Image RegularizationCode0
ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-ExpertsCode0
A Novel Truncated Norm Regularization Method for Multi-channel Color Image DenoisingCode0
Towards More Accurate Diffusion Model Acceleration with A Timestep TunerCode0
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and SemanticsCode0
Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex PenaltiesCode0
Quantum-Inspired Hamiltonian Monte Carlo for Bayesian SamplingCode0
ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge SplittingCode0
TWIST: Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance SegmentationCode0
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse ProblemsCode0
Mesh Denoising with Facet Graph ConvolutionsCode0
Can learning from natural image denoising be used for seismic data interpolation?Code0
On-the-fly Denoising for Data Augmentation in Natural Language UnderstandingCode0
Meta-DiffuB: A Contextualized Sequence-to-Sequence Text Diffusion Model with Meta-ExplorationCode0
Estimating network edge probabilities by neighborhood smoothingCode0
Compensation Sampling for Improved Convergence in Diffusion ModelsCode0
Estimating Probability Densities with Transformer and Denoising DiffusionCode0
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt DenoisingCode0
On the Importance of Denoising when Learning to Compress ImagesCode0
On the Importance of Noise Scheduling for Diffusion ModelsCode0
DeepRED: Deep Image Prior Powered by REDCode0
Metaphor Detection with Effective Context DenoisingCode0
Quaternion Convolutional Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified