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 426–450 of 7282 papers

TitleStatusHype
SF-V: Single Forward Video Generation ModelCode2
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion ModelsCode2
Denoising Diffusion Bridge ModelsCode2
Denoising Diffusion Implicit ModelsCode2
Simple diffusion: End-to-end diffusion for high resolution imagesCode2
Diffusion-based Generation, Optimization, and Planning in 3D ScenesCode2
Denoising Diffusion Probabilistic ModelsCode2
Denoising Diffusion Restoration ModelsCode2
Speech Denoising in the Waveform Domain with Self-AttentionCode2
Diffusion-based Visual Anagram as Multi-task LearningCode2
DiffusionInst: Diffusion Model for Instance SegmentationCode2
Diffusion Transformer PolicyCode2
Streaming Anomaly DetectionCode2
EchoScene: Indoor Scene Generation via Information Echo over Scene Graph DiffusionCode2
On the Generalization of BasicVSR++ to Video Deblurring and DenoisingCode2
SyncTweedies: A General Generative Framework Based on Synchronized DiffusionsCode2
Synthesis of discrete-continuous quantum circuits with multimodal diffusion modelsCode2
An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion ModelsCode2
Diff-UNet: A Diffusion Embedded Network for Volumetric SegmentationCode1
Diffuse3D: Wide-Angle 3D Photography via Bilateral DiffusionCode1
DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic ModelCode1
DiffuseReg: Denoising Diffusion Model for Obtaining Deformation Fields in Unsupervised Deformable Image RegistrationCode1
DiffStyler: Controllable Dual Diffusion for Text-Driven Image StylizationCode1
DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion ModelsCode1
DiffStyler: Diffusion-based Localized Image Style TransferCode1
Show:102550
← PrevPage 18 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