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

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Showing 3751–3775 of 7282 papers

TitleStatusHype
DiffTAD: Temporal Action Detection with Proposal Denoising DiffusionCode1
DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-ConsistencyCode0
Shot Noise Reduction in Radiographic and Tomographic Multi-Channel Imaging with Self-Supervised Deep Learning—0
Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up TablesCode1
DiffuScene: Denoising Diffusion Models for Generative Indoor Scene SynthesisCode0
Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor—0
End-to-End Diffusion Latent Optimization Improves Classifier GuidanceCode1
Controllable Inversion of Black-Box Face Recognition Models via Diffusion—0
Masked Image Training for Generalizable Deep Image DenoisingCode2
Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT—0
Pix2Video: Video Editing using Image DiffusionCode1
E-MLB: Multilevel Benchmark for Event-Based Camera DenoisingCode1
3D Human Mesh Estimation from Virtual MarkersCode2
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image RestorationCode1
Leapfrog Diffusion Model for Stochastic Trajectory PredictionCode2
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data—0
Localizing Object-level Shape Variations with Text-to-Image Diffusion ModelsCode1
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers—0
Pluralistic Aging Diffusion Autoencoder—0
Discovering Interpretable Directions in the Semantic Latent Space of Diffusion ModelsCode1
Diff-UNet: A Diffusion Embedded Network for Volumetric SegmentationCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
Image Statistics Predict the Sensitivity of Perceptual Quality Metrics—0
Adversarial Counterfactual Visual ExplanationsCode1
Deep Nonparametric Estimation of Intrinsic Data Structures by Chart Autoencoders: Generalization Error and Robustness—0
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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