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 4701–4725 of 7282 papers

TitleStatusHype
SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding—0
Content-Noise Complementary Learning for Medical Image DenoisingCode1
Error Correction in ASR using Sequence-to-Sequence Models—0
Posterior temperature optimized Bayesian models for inverse problems in medical imagingCode0
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex RegularizationCode1
Low-Rank Tensor Completion Based on Bivariate Equivalent Minimax-Concave Penalty—0
Practical Noise Simulation for RGB Images—0
Blind ECG Restoration by Operational Cycle-GANsCode1
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study—0
Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI—0
A novel ECG signal denoising filter selection algorithm based on conventional neural networks—0
DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs—0
VRT: A Video Restoration TransformerCode3
Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic ModelCode1
Denoising Diffusion Restoration ModelsCode2
Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network—0
Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling—0
Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed TomographyCode1
Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation—0
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-TrainingCode2
RePaint: Inpainting using Denoising Diffusion Probabilistic ModelsCode4
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream TasksCode1
FN-Net:Remove the Outliers by Filtering the Noise—0
Noise-specific denoising method with applications to high-frequency ultrasonic images—0
Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation—0
Show:102550
← PrevPage 189 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