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 276–300 of 7282 papers

TitleStatusHype
MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy ProjectionsCode2
Hybrid Convolutional and Attention Network for Hyperspectral Image DenoisingCode2
Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-ExpertsCode2
Beyond Text: Frozen Large Language Models in Visual Signal ComprehensionCode2
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-TrainingCode2
NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and MergingCode2
Dual-domain strip attention for image restorationCode2
ViewFusion: Towards Multi-View Consistency via Interpolated DenoisingCode2
DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D ReassemblyCode2
HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion ModelsCode2
GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising DiffusionCode2
RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion ModelsCode2
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network GenerationCode2
DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image PersonalizationCode2
Iterated Denoising Energy Matching for Sampling from Boltzmann DensitiesCode2
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
Time Series Diffusion in the Frequency DomainCode2
Blue noise for diffusion modelsCode2
Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object DetectionCode2
Guidance with Spherical Gaussian Constraint for Conditional DiffusionCode2
Improving Diffusion Models for Inverse Problems Using Optimal Posterior CovarianceCode2
Cross-view Masked Diffusion Transformers for Person Image SynthesisCode2
AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction ErrorCode2
BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane ExtrapolationCode2
CascadedGaze: Efficiency in Global Context Extraction for Image RestorationCode2
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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