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 1126–1150 of 7282 papers

TitleStatusHype
SOWing Information: Cultivating Contextual Coherence with MLLMs in Image Generation—0
Structured Object Language Modeling (SoLM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising—0
VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference—0
Towards a Mechanistic Explanation of Diffusion Model Generalization—0
Limit Order Book Event Stream Prediction with Diffusion Model—0
Unpacking the Individual Components of Diffusion Policy—0
Steering Rectified Flow Models in the Vector Field for Controlled Image Generation—0
Towards Chunk-Wise Generation for Long Videos—0
Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion ModelsCode0
MatchDiffusion: Training-free Generation of Match-cutsCode1
Enhancing MMDiT-Based Text-to-Image Models for Similar Subject GenerationCode1
Prediction with Action: Visual Policy Learning via Joint Denoising Process—0
Omegance: A Single Parameter for Various Granularities in Diffusion-Based SynthesisCode2
Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral ConstraintsCode2
StableAnimator: High-Quality Identity-Preserving Human Image AnimationCode5
Optimal Estimation of Shared Singular Subspaces across Multiple Noisy Matrices—0
Reward Incremental Learning in Text-to-Image Generation—0
SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates—0
Contrastive CFG: Improving CFG in Diffusion Models by Contrasting Positive and Negative Concepts—0
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-ResolutionCode2
Can LLMs be Good Graph Judge for Knowledge Graph Construction?Code1
Mixed-State Quantum Denoising Diffusion Probabilistic Model—0
Revisiting DDIM Inversion for Controlling Defect Generation by Disentangling the Background—0
NovelGS: Consistent Novel-view Denoising via Large Gaussian Reconstruction Model—0
Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observation—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