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 1901–1925 of 7282 papers

TitleStatusHype
Brightness-Invariant Tracking Estimation in Tagged MRI—0
Towards more transferable adversarial attack in black-box manner—0
CONCORD: Concept-Informed Diffusion for Dataset DistillationCode0
Applications of Modular Co-Design for De Novo 3D Molecule Generation—0
CIM-NET: A Video Denoising Deep Neural Network Model Optimized for Computing-in-Memory Architectures—0
SHaDe: Compact and Consistent Dynamic 3D Reconstruction via Tri-Plane Deformation and Latent Diffusion—0
TRAIL: Transferable Robust Adversarial Images via Latent diffusion—0
Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction—0
Sufficient conditions for offline reactivation in recurrent neural networksCode0
Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation—0
Joint Flow And Feature Refinement Using Attention For Video Restoration—0
Creatively Upscaling Images with Global-Regional Priors—0
Consistent World Models via Foresight Diffusion—0
Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models—0
Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering—0
Toward Theoretical Insights into Diffusion Trajectory Distillation via Operator Merging—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
Adaptive Estimation and Learning under Temporal Distribution Shift—0
Decoding Phone Pairs from MEG Signals Across Speech ModalitiesCode0
Image-to-Image Translation with Diffusion Transformers and CLIP-Based Image Conditioning—0
Diffusion Probabilistic Generative Models for Accelerated, in-NICU Permanent Magnet Neonatal MRI—0
Multi-Channel Swin Transformer Framework for Bearing Remaining Useful Life Prediction—0
Adaptive Cyclic Diffusion for Inference Scaling—0
RefiDiff: Refinement-Aware Diffusion for Efficient Missing Data Imputation—0
Neural Inverse Scattering with Score-based Regularization—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