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 3751–3775 of 7282 papers

TitleStatusHype
Non-Denoising Forward-Time Diffusions—0
Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion ModelsCode0
Tuning-Free Inversion-Enhanced Control for Consistent Image Editing—0
Balancing the Style-Content Trade-Off in Sentiment Transfer Using Polarity-Aware DenoisingCode0
Multi-Sentence Grounding for Long-term Instructional Video—0
Fine-grained Forecasting Models Via Gaussian Process Blurring EffectCode0
Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction—0
Adaptive Training Meets Progressive Scaling: Elevating Efficiency in Diffusion Models—0
Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model—0
Class Information Guided Reconstruction for Automatic Modulation Open-Set Recognition—0
Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation—0
GazeMoDiff: Gaze-guided Diffusion Model for Stochastic Human Motion Prediction—0
On Inference Stability for Diffusion ModelsCode0
Optimizing Diffusion Noise Can Serve As Universal Motion Priors—0
Diffusing More Objects for Semi-Supervised Domain Adaptation with Less Labeling—0
Active contours driven by local and global intensity fitting energy with application to SAR image segmentation and its fast solvers—0
MagicScroll: Nontypical Aspect-Ratio Image Generation for Visual Storytelling via Multi-Layered Semantic-Aware Denoising—0
Towards Detailed Text-to-Motion Synthesis via Basic-to-Advanced Hierarchical Diffusion Model—0
Unified framework for diffusion generative models in SO(3): applications in computer vision and astrophysics—0
A novel diffusion recommendation algorithm based on multi-scale cnn and residual lstm—0
PolyDiff: Generating 3D Polygonal Meshes with Diffusion Models—0
Bayesian ECG reconstruction using denoising diffusion generative models—0
SPIRE: Semantic Prompt-Driven Image Restoration—0
Unraveling the Temporal Dynamics of the Unet in Diffusion Models—0
Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO—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