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 1326–1350 of 7282 papers

TitleStatusHype
Diffusion Posterior Proximal Sampling for Image RestorationCode1
A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large ShiftCode1
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
4D Facial Expression Diffusion ModelCode1
Information-Theoretic DiffusionCode1
-Diff: Infinite Resolution Diffusion with Subsampled Mollified StatesCode1
Diffusion Suction Grasping with Large-Scale Parcel DatasetCode1
Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class LabelsCode1
A Differentiable Perceptual Audio Metric Learned from Just Noticeable DifferencesCode1
Input Perturbation Reduces Exposure Bias in Diffusion ModelsCode1
Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct SupervisionCode1
Decoder Denoising Pretraining for Semantic SegmentationCode1
DiffX: Guide Your Layout to Cross-Modal Generative ModelingCode1
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score MatchingCode1
Digging into contrastive learning for robust depth estimation with diffusion modelsCode1
Inference-Time Text-to-Video Alignment with Diffusion Latent Beam SearchCode1
Civil Rephrases Of Toxic Texts With Self-Supervised TransformersCode1
A CNN-Based Blind Denoising Method for Endoscopic ImagesCode1
DeFT-AN: Dense Frequency-Time Attentive Network for Multichannel Speech EnhancementCode1
Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic ModelingCode1
Input Similarity from the Neural Network PerspectiveCode1
Model Reveals What to Cache: Profiling-Based Feature Reuse for Video Diffusion ModelsCode1
Intermediate Layer Optimization for Inverse Problems using Deep Generative ModelsCode1
DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image GenerationCode1
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image PriorCode1
Show:102550
← PrevPage 54 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