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 851–875 of 7282 papers

TitleStatusHype
Controlling Latent Diffusion Using Latent CLIPCode1
DiffDA: a Diffusion Model for Weather-scale Data AssimilationCode1
Adaptive Graph Contrastive Learning for RecommendationCode1
Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEGCode1
Exploring the Devil in Graph Spectral Domain for 3D Point Cloud AttacksCode1
Explaining generative diffusion models via visual analysis for interpretable decision-making processCode1
DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion ModelsCode1
Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic CamerasCode1
Exploration of Lightweight Single Image Denoising with Transformers and Truly Fair TrainingCode1
An Analysis and Mitigation of the Reversal CurseCode1
Adversarial score matching and improved sampling for image generationCode1
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based PenaltiesCode1
Exploring Diffusion Time-steps for Unsupervised Representation LearningCode1
Exploring Versatile Prior for Human Motion via Motion Frequency GuidanceCode1
BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial NetworksCode1
DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging LoadCode1
Continuous Speculative Decoding for Autoregressive Image GenerationCode1
Adversarial Schrödinger Bridge MatchingCode1
DiffPortrait3D: Controllable Diffusion for Zero-Shot Portrait View SynthesisCode1
Beta DiffusionCode1
Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion ModelsCode1
Better Diffusion Models Further Improve Adversarial TrainingCode1
DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraintsCode1
DiffSF: Diffusion Models for Scene Flow EstimationCode1
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised h-transformCode1
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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