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 2451–2475 of 7282 papers

TitleStatusHype
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking—0
MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation—0
LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignment—0
TDM: Temporally-Consistent Diffusion Model for All-in-One Real-World Video Restoration—0
Time Series Language Model for Descriptive Caption Generation—0
Contrastive Learning Augmented Social RecommendationsCode0
Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things—0
Optimizing Noise Schedules of Generative Models in High Dimensionss—0
InDeed: Interpretable image deep decomposition with guaranteed generalizability—0
PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation—0
Cached Adaptive Token Merging: Dynamic Token Reduction and Redundant Computation Elimination in Diffusion ModelCode0
Towards Precise Embodied Dialogue Localization via Causality Guided Diffusion—0
LITA-GS: Illumination-Agnostic Novel View Synthesis via Reference-Free 3D Gaussian Splatting and Physical Priors—0
PDFactor: Learning Tri-Perspective View Policy Diffusion Field for Multi-Task Robotic Manipulation—0
RaSS: Improving Denoising Diffusion Samplers with Reinforced Active Sampling Scheduler—0
Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning—0
Fingerprinting Denoising Diffusion Probabilistic Models—0
Hierarchical Flow Diffusion for Efficient Frame Interpolation—0
Satellite to GroundScape - Large-scale Consistent Ground View Generation from Satellite Views—0
Beyond Human Perception: Understanding Multi-Object World from Monocular ViewCode0
Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images—0
Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos—0
STINR: Deciphering Spatial Transcriptomics via Implicit Neural Representation—0
Unboxed: Geometrically and Temporally Consistent Video Outpainting—0
Encapsulated Composition of Text-to-Image and Text-to-Video Models for High-Quality Video Synthesis—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