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

Showing 226250 of 7282 papers

TitleStatusHype
HumanMAC: Masked Motion Completion for Human Motion PredictionCode2
Diffusion Transformer PolicyCode2
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity RecognitionCode2
DiffusionTrack: Diffusion Model For Multi-Object TrackingCode2
IDOL: Unified Dual-Modal Latent Diffusion for Human-Centric Joint Video-Depth GenerationCode2
CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency ModelCode2
Immiscible Diffusion: Accelerating Diffusion Training with Noise AssignmentCode2
Collaborative Diffusion for Multi-Modal Face Generation and EditingCode2
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelCode2
Diffusion Recommender ModelCode2
Improving Diffusion Inverse Problem Solving with Decoupled Noise AnnealingCode2
Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory SharpeningCode2
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial OptimizationCode2
Diffusion Models in Vision: A SurveyCode2
Compressed Image Generation with Denoising Diffusion Codebook ModelsCode2
Diffusion Predictive Control with ConstraintsCode2
Compression-Aware One-Step Diffusion Model for JPEG Artifact RemovalCode2
InterGen: Diffusion-based Multi-human Motion Generation under Complex InteractionsCode2
Graph Diffusion Transformers for Multi-Conditional Molecular GenerationCode2
Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral ConstraintsCode2
Diffusion models as plug-and-play priorsCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
Large Language Models are Efficient Learners of Noise-Robust Speech RecognitionCode2
Latent Video Diffusion Models for High-Fidelity Long Video GenerationCode2
DiffusionInst: Diffusion Model for Instance SegmentationCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified