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 551–575 of 7282 papers

TitleStatusHype
Controlling Latent Diffusion Using Latent CLIPCode1
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising AutoencodersCode1
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based PenaltiesCode1
Diffusion Priors for Variational Likelihood Estimation and Image DenoisingCode1
Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure TimesCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Contrastive Denoising Score for Text-guided Latent Diffusion Image EditingCode1
Diffusion Posterior Illumination for Ambiguity-aware Inverse RenderingCode1
Continual Learning of Diffusion Models with Generative DistillationCode1
Continuous Speculative Decoding for Autoregressive Image GenerationCode1
Diffusion Posterior Proximal Sampling for Image RestorationCode1
Adaptive Graph Contrastive Learning for RecommendationCode1
ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow PredictionCode1
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image SegmentationCode1
Content-Noise Complementary Learning for Medical Image DenoisingCode1
Diffusion Models Learn Low-Dimensional Distributions via Subspace ClusteringCode1
DiffusionNER: Boundary Diffusion for Named Entity RecognitionCode1
Diffusion Models for Medical Anomaly DetectionCode1
Diffusion Models for Graphs Benefit From Discrete State SpacesCode1
Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be ConsistentCode1
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain ImagesCode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet ExtractionCode1
3D Brain and Heart Volume Generative Models: A SurveyCode1
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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