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 4076–4100 of 7282 papers

TitleStatusHype
Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery—0
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space ModelCode4
Multi-Class Segmentation from Aerial Views using Recursive Noise DiffusionCode1
Denoising Deep Generative ModelsCode0
DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded LibrariesCode1
Gradient Domain Weighted Guided Image Filtering—0
Denoising Diffusion for Sampling SAT Solutions—0
Linking Sketch Patches by Learning Synonymous Proximity for Graphic Sketch RepresentationCode0
SinDDM: A Single Image Denoising Diffusion ModelCode1
CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback—0
Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus—0
Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion—0
Unsupervised Visual Defect Detection with Score-Based Generative Model—0
Learn to See Faster: Pushing the Limits of High-Speed Camera with Deep Underexposed Image Denoising—0
Post-training Quantization on Diffusion ModelsCode1
Refining Generative Process with Discriminator Guidance in Score-based Diffusion ModelsCode1
BJTU-WeChat's Systems for the WMT22 Chat Translation Task—0
Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising AutoencodersCode0
DiffusionBERT: Improving Generative Masked Language Models with Diffusion ModelsCode2
Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep PriorsCode0
Improved Quasi-Recurrent Neural Network for Hyperspectral Image Denoising—0
Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration—0
CFNet: Conditional Filter Learning with Dynamic Noise Estimation for Real Image Denoising—0
3DDesigner: Towards Photorealistic 3D Object Generation and Editing with Text-guided Diffusion Models—0
Spatial-Spectral Transformer for Hyperspectral Image DenoisingCode1
Show:102550
← PrevPage 164 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