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 4251–4275 of 7282 papers

TitleStatusHype
Programmable Annotation with Diversed Heuristics and Data Denoising—0
CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation ExtractionCode0
Adaptive Feature Discrimination and Denoising for Asymmetric Text Matching—0
Data Synthesis and Iterative Refinement for Neural Semantic Parsing without Annotated Logical Forms—0
Towards Making the Most of Pre-trained Translation Model for Quality Estimation—0
Diagnostic Quality Assessment for Low-Dimensional ECG Representations—0
Protein structure generation via folding diffusionCode2
TT-NF: Tensor Train Neural FieldsCode1
TabDDPM: Modelling Tabular Data with Diffusion ModelsCode2
Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions—0
Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20 Speedup—0
How Powerful is Implicit Denoising in Graph Neural Networks—0
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis—0
Bidirectional Language Models Are Also Few-shot Learners—0
Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation—0
R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 ClassificationCode0
DreamFusion: Text-to-3D using 2D DiffusionCode5
DiGress: Discrete Denoising diffusion for graph generationCode2
Denoising MCMC for Accelerating Diffusion-Based Generative ModelsCode1
Deep Sparse and Low-Rank Prior for Hyperspectral Image DenoisingCode0
Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidenceCode1
Speech Enhancement Using Self-Supervised Pre-Trained Model and Vector Quantization—0
Efficient Image Denoising by Low-Rank Singular Vector Approximations of Geodesics' Gramian Matrix—0
Scaling Laws For Deep Learning Based Image ReconstructionCode0
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness—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