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 2476–2500 of 7282 papers

TitleStatusHype
Align-A-Video: Deterministic Reward Tuning of Image Diffusion Models for Consistent Video Editing—0
LITA-GS: Illumination-Agnostic Novel View Synthesis via Reference-Free 3D Gaussian Splatting and Physical Priors—0
Fingerprinting Denoising Diffusion Probabilistic Models—0
Encapsulated Composition of Text-to-Image and Text-to-Video Models for High-Quality Video Synthesis—0
Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning—0
Multi-Modal Contrastive Masked Autoencoders: A Two-Stage Progressive Pre-training Approach for RGBD Datasets—0
SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation—0
FeedEdit: Text-Based Image Editing with Dynamic Feedback Regulation—0
TFCustom: Customized Image Generation with Time-Aware Frequency Feature Guidance—0
CoordFlow: Coordinate Flow for Pixel-wise Neural Video Representation—0
Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning—0
EdgeDiff: Edge-aware Diffusion Network for Building Reconstruction from Point Clouds—0
APT: Adaptive Personalized Training for Diffusion Models with Limited Data—0
VODiff: Controlling Object Visibility Order in Text-to-Image Generation—0
V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection—0
Random Conditioning for Diffusion Model Compression with Distillation—0
DriveScape: High-Resolution Driving Video Generation by Multi-View Feature Fusion—0
Composing Parts for Expressive Object Generation—0
Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos—0
Towards Precise Embodied Dialogue Localization via Causality Guided Diffusion—0
Unboxed: Geometrically and Temporally Consistent Video Outpainting—0
RaSS: Improving Denoising Diffusion Samplers with Reinforced Active Sampling Scheduler—0
Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observations—0
Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion—0
STINR: Deciphering Spatial Transcriptomics via Implicit Neural Representation—0
Show:102550
← PrevPage 100 of 292Next →
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