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 6776–6800 of 7282 papers

TitleStatusHype
GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT DenoisingCode0
Skeleton-Based Human Action Recognition with Noisy LabelsCode0
Structured Uncertainty Prediction NetworksCode0
Noise Robust Generative Adversarial NetworksCode0
A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocksCode0
Noise-Robust Keyword Spotting through Self-supervised PretrainingCode0
Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative ModelsCode0
Domain-Adversarial Neural NetworksCode0
Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion ModelsCode0
Acceleration of RED via Vector ExtrapolationCode0
Pretraining Strategy for Neural PotentialsCode0
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited DataCode0
Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising ModelCode0
NoiseTransfer: Image Noise Generation with Contrastive EmbeddingsCode0
Unified 3D MRI Representations via Sequence-Invariant Contrastive LearningCode0
Noisier2Noise: Learning to Denoise from Unpaired Noisy DataCode0
Deep CSI Learning for Gait Biometric Sensing and RecognitionCode0
Advancing Medical Image Segmentation: Morphology-Driven Learning with Diffusion TransformerCode0
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution PipelineCode0
Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image DenoisingCode0
Advancing low-field MRI with a universal denoising imaging transformer: Towards fast and high-quality imagingCode0
DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET ImagingCode0
Accelerated First Order Methods for Variational ImagingCode0
Double Correction Framework for Denoising RecommendationCode0
Accelerated Cardiac Parametric Mapping using Deep Learning-Refined Subspace ModelsCode0
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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