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
Aligning Text-to-Image Diffusion Models with Reward BackpropagationCode2
HumanMAC: Masked Motion Completion for Human Motion PredictionCode2
Human Motion Diffusion as a Generative PriorCode2
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity RecognitionCode2
Coherent and Multi-modality Image Inpainting via Latent Space OptimizationCode2
DiGress: Discrete Denoising diffusion for graph generationCode2
DiffusionTrack: Diffusion Model For Multi-Object TrackingCode2
Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory SharpeningCode2
Immiscible Diffusion: Accelerating Diffusion Training with Noise AssignmentCode2
Diffusion Transformer PolicyCode2
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelCode2
Improved Vector Quantized Diffusion ModelsCode2
Improving Diffusion Inverse Problem Solving with Decoupled Noise AnnealingCode2
Diffusion Recommender ModelCode2
Consistency Diffusion Bridge ModelsCode2
Compression-Aware One-Step Diffusion Model for JPEG Artifact RemovalCode2
Discrete Diffusion Modeling by Estimating the Ratios of the Data DistributionCode2
Diffusion Models in Vision: A SurveyCode2
Inversion-Based Style Transfer with Diffusion ModelsCode2
Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral ConstraintsCode2
Diffusion Predictive Control with ConstraintsCode2
L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object DetectionCode2
Conditional Image Synthesis with Diffusion Models: A SurveyCode2
Diffusion models as plug-and-play priorsCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
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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