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 1276–1300 of 7282 papers

TitleStatusHype
Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric VideosCode1
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based PenaltiesCode1
Focus on Your Instruction: Fine-grained and Multi-instruction Image Editing by Attention ModulationCode1
A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large ShiftCode1
Content-Noise Complementary Learning for Medical Image DenoisingCode1
DiffO: Single-step Diffusion for Image Compression at Ultra-Low BitratesCode1
Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEGCode1
Adversarial purification with Score-based generative modelsCode1
From Denoising to Compressed SensingCode1
Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class LabelsCode1
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image RestorationCode1
3D Vessel Graph Generation Using Denoising DiffusionCode1
Fully Spiking Denoising Diffusion Implicit ModelsCode1
Gabor is Enough: Interpretable Deep Denoising with a Gabor Synthesis Dictionary PriorCode1
Continual Learning of Diffusion Models with Generative DistillationCode1
A Differentiable Perceptual Audio Metric Learned from Just Noticeable DifferencesCode1
DiffDA: a Diffusion Model for Weather-scale Data AssimilationCode1
Differentiable Manifold Reconstruction for Point Cloud DenoisingCode1
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score MatchingCode1
DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion ModelCode1
Adversarial score matching and improved sampling for image generationCode1
Civil Rephrases Of Toxic Texts With Self-Supervised TransformersCode1
Generalized Low Rank ModelsCode1
A CNN-Based Blind Denoising Method for Endoscopic ImagesCode1
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
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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