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 2651–2675 of 7282 papers

TitleStatusHype
SOWing Information: Cultivating Contextual Coherence with MLLMs in Image Generation—0
Towards a Mechanistic Explanation of Diffusion Model Generalization—0
Random Sampling for Diffusion-based Adversarial PurificationCode0
Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution—0
Steering Rectified Flow Models in the Vector Field for Controlled Image Generation—0
Unpacking the Individual Components of Diffusion Policy—0
Limit Order Book Event Stream Prediction with Diffusion Model—0
Towards Chunk-Wise Generation for Long Videos—0
Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion ModelsCode0
Prediction with Action: Visual Policy Learning via Joint Denoising Process—0
Reward Incremental Learning in Text-to-Image Generation—0
Optimal Estimation of Shared Singular Subspaces across Multiple Noisy Matrices—0
Contrastive CFG: Improving CFG in Diffusion Models by Contrasting Positive and Negative Concepts—0
SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates—0
Mixed-State Quantum Denoising Diffusion Probabilistic Model—0
DiffDesign: Controllable Diffusion with Meta Prior for Efficient Interior Design Generation—0
Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory—0
NovelGS: Consistent Novel-view Denoising via Large Gaussian Reconstruction Model—0
Revisiting DDIM Inversion for Controlling Defect Generation by Disentangling the Background—0
Controllable Human Image Generation with Personalized Multi-Garments—0
Comparison of Generative Learning Methods for Turbulence Modeling—0
Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observation—0
MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning—0
MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing—0
PriorDiffusion: Leverage Language Prior in Diffusion Models for Monocular Depth Estimation—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