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 6726–6750 of 7282 papers

TitleStatusHype
Dual-Camera Joint Deblurring-Denoising—0
Subspace Hybrid Beamforming for Head-worn Microphone Arrays—0
Subspace modeling for fast and high-sensitivity X-ray chemical imaging—0
Dual Diffusion Architecture for Fisheye Image Rectification: Synthetic-to-Real Generalization—0
Subsurface defect imaging in PZT ceramics using dual point contact excitation and detection—0
A hybrid neuro--wavelet predictor for QoS control and stability—0
SubZero: Composing Subject, Style, and Action via Zero-Shot Personalization—0
Dual-domain Collaborative Denoising for Social Recommendation—0
A hybrid approach to seismic deblending: when physics meets self-supervision—0
DualNeRF: Text-Driven 3D Scene Editing via Dual-Field Representation—0
Dual Prompting for Diverse Count-level PET Denoising—0
DualReal: Adaptive Joint Training for Lossless Identity-Motion Fusion in Video Customization—0
Dual reparametrized Variational Generative Model for Time-Series Forecasting—0
SUD^2: Supervision by Denoising Diffusion Models for Image Reconstruction—0
Supervision by Denoising for Medical Image Segmentation—0
DuInNet: Dual-Modality Feature Interaction for Point Cloud Completion—0
DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesis—0
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training—0
SUMD: Super U-shaped Matrix Decomposition Convolutional neural network for Image denoising—0
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration—0
3D Point Cloud Denoising via Bipartite Graph Approximation and Reweighted Graph Laplacian—0
Dynamic Dual-Output Diffusion Models—0
Dynamic Low-light Imaging with Quanta Image Sensors—0
Dynamic Low-light Imaging with Quanta Image Sensors—0
Dynamic Point Cloud Denoising via Gradient Fields—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