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 576–600 of 7282 papers

TitleStatusHype
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based PenaltiesCode1
Diffusion Suction Grasping with Large-Scale Parcel DatasetCode1
DiffX: Guide Your Layout to Cross-Modal Generative ModelingCode1
Discovering Interpretable Directions in the Semantic Latent Space of Diffusion ModelsCode1
Divide-and-Conquer Posterior Sampling for Denoising Diffusion PriorsCode1
Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time StepsCode1
Diffusion Priors for Variational Likelihood Estimation and Image DenoisingCode1
Diffusion Probabilistic Modeling for Video GenerationCode1
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual PerceptionCode1
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain NavigationCode1
Contrastive Denoising Score for Text-guided Latent Diffusion Image EditingCode1
Aligning Few-Step Diffusion Models with Dense Reward Difference LearningCode1
Diffusion Posterior Proximal Sampling for Image RestorationCode1
Continual Learning of Diffusion Models with Generative DistillationCode1
3D^2-Actor: Learning Pose-Conditioned 3D-Aware Denoiser for Realistic Gaussian Avatar ModelingCode1
Continuous Speculative Decoding for Autoregressive Image GenerationCode1
A Light and Tuning-free Method for Simulating Camera Motion in Video GenerationCode1
Adaptive Differential Denoising for Respiratory Sounds ClassificationCode1
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image SegmentationCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Content-Noise Complementary Learning for Medical Image DenoisingCode1
Diffusion Models Learn Low-Dimensional Distributions via Subspace ClusteringCode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
A Cat Is A Cat (Not A Dog!): Unraveling Information Mix-ups in Text-to-Image Encoders through Causal Analysis and Embedding OptimizationCode1
DiffusionNER: Boundary Diffusion for Named Entity RecognitionCode1
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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