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 26262650 of 7282 papers

TitleStatusHype
GeomCLIP: Contrastive Geometry-Text Pre-training for MoleculesCode0
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational PosteriorsCode0
Cloud K-SVD for Image DenoisingCode0
GenPlan: Generative Sequence Models as Adaptive PlannersCode0
Cloud Dictionary: Sparse Coding and Modeling for Point CloudsCode0
Geometric-Facilitated Denoising Diffusion Model for 3D Molecule GenerationCode0
DiffusionTrack: Point Set Diffusion Model for Visual Object TrackingCode0
Generative Simulations of The Solar Corona Evolution With Denoising Diffusion : Proof of ConceptCode0
Generative Plug and Play: Posterior Sampling for Inverse ProblemsCode0
Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation StudyCode0
Graph Signal Recovery Using Restricted Boltzmann MachinesCode0
Generative Flows as a General Purpose Solution for Inverse ProblemsCode0
Enhancing Image Resolution of Solar Magnetograms: A Latent Diffusion Model ApproachCode0
CLMB: deep contrastive learning for robust metagenomic binningCode0
Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample FilteringCode0
Generative Modeling of Microweather Wind Velocities for Urban Air MobilityCode0
Generating Training Data for Denoising Real RGB Images via Camera Pipeline SimulationCode0
Generative Modeling of Seismic Data using Diffusion Models and its Application to Multi-Purpose Seismic Inverse ProblemsCode0
Generating observation guided ensembles for data assimilation with denoising diffusion probabilistic modelCode0
Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative PriorsCode0
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-DecodingCode0
Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot LearningCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
Anomaly Detection with Robust Deep AutoencodersCode0
Generalized Robust Fundus Photography-based Vision Loss Estimation for High MyopiaCode0
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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