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 676–700 of 7282 papers

TitleStatusHype
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from ImageCode1
COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video EditingCode1
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
DS-Fusion: Artistic Typography via Discriminated and Stylized DiffusionCode1
Dual convolutional neural network with attention for image blind denoisingCode1
DVIS++: Improved Decoupled Framework for Universal Video SegmentationCode1
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and GeneralizationCode1
DR2: Diffusion-based Robust Degradation Remover for Blind Face RestorationCode1
DPMesh: Exploiting Diffusion Prior for Occluded Human Mesh RecoveryCode1
DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework for Spelling Error Correction of Bangla and Resource Scarce Indic LanguagesCode1
DPM-OT: A New Diffusion Probabilistic Model Based on Optimal TransportCode1
DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion ModelCode1
DogLayout: Denoising Diffusion GAN for Discrete and Continuous Layout GenerationCode1
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language ModelsCode1
Domain Generalization for Object Recognition with Multi-task AutoencodersCode1
Controlling Latent Diffusion Using Latent CLIPCode1
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based PenaltiesCode1
Don't Play Favorites: Minority Guidance for Diffusion ModelsCode1
DoseDiff: Distance-aware Diffusion Model for Dose Prediction in RadiotherapyCode1
Convolutional Proximal Neural Networks and Plug-and-Play AlgorithmsCode1
DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the WildCode1
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented GenerationCode1
DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-TransformerCode1
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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