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 7226–7250 of 7282 papers

TitleStatusHype
Face Morphing Attack Detection with Denoising Diffusion Probabilistic ModelsCode0
Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging ProblemsCode0
Instance Regularization for Discriminative Language Model Pre-trainingCode0
Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and DestripingCode0
A Comprehensive Comparison of Multi-Dimensional Image Denoising MethodsCode0
Deep Mean-Shift Priors for Image RestorationCode0
Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion ModelCode0
A Dictionary Based Approach for Removing Out-of-Focus BlurCode0
FAP-CD: Fairness-Driven Age-Friendly Community Planning via Conditional Diffusion GenerationCode0
Bayes-optimal learning of an extensive-width neural network from quadratically many samplesCode0
Real Image Denoising with Feature AttentionCode0
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic ModelsCode0
FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder ParadigmCode0
Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T TrackingCode0
Two-stage Progressive Residual Dense Attention Network for Image DenoisingCode0
Fast Algorithm for Constrained Linear Inverse ProblemsCode0
Compression Artifacts Reduction by a Deep Convolutional NetworkCode0
Fast and Differentiable Message Passing on Pairwise Markov Random FieldsCode0
Fast and Effective L0 Gradient Minimization by Region FusionCode0
Type Information-Assisted Self-Supervised Knowledge Graph DenoisingCode0
Interacting Diffusion Processes for Event Sequence ForecastingCode0
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary LearningCode0
Scheduled denoising autoencodersCode0
Model-blind Video Denoising Via Frame-to-frame TrainingCode0
DeepOrientation: convolutional neural network for fringe pattern orientation map estimationCode0
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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