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

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Showing 876–900 of 7282 papers

TitleStatusHype
LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignment—0
DepthMaster: Taming Diffusion Models for Monocular Depth EstimationCode2
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking—0
MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation—0
MagicFace: High-Fidelity Facial Expression Editing with Action-Unit ControlCode2
TDM: Temporally-Consistent Diffusion Model for All-in-One Real-World Video Restoration—0
DiffGraph: Heterogeneous Graph Diffusion ModelCode2
Contrastive Learning Augmented Social RecommendationsCode0
Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things—0
Time Series Language Model for Descriptive Caption Generation—0
PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation—0
InDeed: Interpretable image deep decomposition with guaranteed generalizability—0
Optimizing Noise Schedules of Generative Models in High Dimensionss—0
Conditional Consistency Guided Image Translation and EnhancementCode1
Satellite to GroundScape - Large-scale Consistent Ground View Generation from Satellite Views—0
Align-A-Video: Deterministic Reward Tuning of Image Diffusion Models for Consistent Video Editing—0
Composing Parts for Expressive Object Generation—0
PDFactor: Learning Tri-Perspective View Policy Diffusion Field for Multi-Task Robotic Manipulation—0
Unboxed: Geometrically and Temporally Consistent Video Outpainting—0
Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold NetworkCode0
TFCustom: Customized Image Generation with Time-Aware Frequency Feature Guidance—0
DriveScape: High-Resolution Driving Video Generation by Multi-View Feature Fusion—0
Fingerprinting Denoising Diffusion Probabilistic Models—0
Towards Precise Embodied Dialogue Localization via Causality Guided Diffusion—0
FeedEdit: Text-Based Image Editing with Dynamic Feedback Regulation—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