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 801–825 of 7282 papers

TitleStatusHype
Entity-Level Text-Guided Image ManipulationCode1
Entropy-driven Sampling and Training Scheme for Conditional Diffusion GenerationCode1
Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion ModelsCode1
EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within TransformerCode1
Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic CamerasCode1
Explaining generative diffusion models via visual analysis for interpretable decision-making processCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder ArchitectureCode1
A Light and Tuning-free Method for Simulating Camera Motion in Video GenerationCode1
Exploring the Devil in Graph Spectral Domain for 3D Point Cloud AttacksCode1
DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein TracesCode1
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic ModelsCode1
Extended U-Net for Speaker Verification in Noisy EnvironmentsCode1
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text GenerationCode1
DenoMamba: A fused state-space model for low-dose CT denoisingCode1
Denoising Relation Extraction from Document-level Distant SupervisionCode1
Argmax Flows and Multinomial Diffusion: Learning Categorical DistributionsCode1
A Variational Perspective on Solving Inverse Problems with Diffusion ModelsCode1
Denoising Task Routing for Diffusion ModelsCode1
Diffiner: A Versatile Diffusion-based Generative Refiner for Speech EnhancementCode1
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual PerceptionCode1
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation LearningCode1
Denoising Normalizing FlowCode1
Fast mesh denoising with data driven normal filtering using deep variational autoencodersCode1
Denoising Multi-Source Weak Supervision for Neural Text ClassificationCode1
Show:102550
← PrevPage 33 of 292Next →
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