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 2326–2350 of 7282 papers

TitleStatusHype
Controllable Motion Generation via Diffusion Modal CouplingCode0
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-ClassificationCode0
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and SemanticsCode0
Improving Hypernymy Extraction with Distributional Semantic ClassesCode0
Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-TuningCode0
Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced ErrorCode0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular DynamicsCode0
ImPoster: Text and Frequency Guidance for Subject Driven Action Personalization using Diffusion ModelsCode0
Contrastive Sequential-Diffusion Learning: Non-linear and Multi-Scene Instructional Video SynthesisCode0
Contrastive Principal Component AnalysisCode0
A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood FiltersCode0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
Implicit 3D Orientation Learning for 6D Object Detection from RGB ImagesCode0
Natural Image Noise DatasetCode0
Imaging at the quantum limit with convolutional neural networksCode0
Contrastive Conditional Latent Diffusion for Audio-visual SegmentationCode0
Are We Using Autoencoders in a Wrong Way?Code0
Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomyCode0
Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image GenerationCode0
Image Segmentation by Iterative Inference from Conditional Score EstimationCode0
Image-to-Image MLP-mixer for Image ReconstructionCode0
Improved Diffusion-based Generative Model with Better Adversarial RobustnessCode0
Image Restoration using Plug-and-Play CNN MAP DenoisersCode0
Image Restoration Using Deep Regulated Convolutional NetworksCode0
Show:102550
← PrevPage 94 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