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 2076–2100 of 7282 papers

TitleStatusHype
HD-KT: Advancing Robust Knowledge Tracing via Anomalous Learning Interaction DetectionCode1
Do Bayesian imaging methods report trustworthy probabilities?—0
FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival—0
SAR Image Synthesis with Diffusion Models—0
Modeling Pedestrian Intrinsic Uncertainty for Multimodal Stochastic Trajectory Prediction via Energy Plan Denoising—0
Input Snapshots Fusion for Scalable Discrete Dynamic Graph Nerual Networks—0
Deep MMD Gradient Flow without adversarial training—0
Self-Consistent Recursive Diffusion Bridge for Medical Image TranslationCode2
Compression-Realized Deep Structural Network for Video Quality Enhancement—0
Mesh Denoising Transformer—0
Array SAR 3D Sparse Imaging Based on Regularization by Denoising Under Few Observed Data—0
supDQN: Supervised Rewarding Strategy Driven Deep Q-Network for sEMG Signal Decontamination—0
StableMoFusion: Towards Robust and Efficient Diffusion-based Motion Generation Framework—0
SubGDiff: A Subgraph Diffusion Model to Improve Molecular Representation LearningCode1
Fast LiDAR Upsampling using Conditional Diffusion ModelsCode0
Dual-domain Collaborative Denoising for Social Recommendation—0
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation—0
FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimation via Diffusion ModelsCode2
Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models—0
Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric VideosCode1
Structure-based drug design by denoising voxel gridsCode1
AdsorbDiff: Adsorbate Placement via Conditional Denoising DiffusionCode1
Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction—0
All-in-One Deep Learning Framework for MR Image Reconstruction—0
Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models—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