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 826–850 of 7282 papers

TitleStatusHype
A zero-inflated gamma model for deconvolved calcium imaging tracesCode1
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data GenerationCode1
Few-Shot Diffusion ModelsCode1
Backpropagation-Friendly EigendecompositionCode1
Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time StepsCode1
Denoising Point Clouds in Latent Space via Graph Convolution and Invertible Neural NetworkCode1
Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidenceCode1
Denoising Relation Extraction from Document-level Distant SupervisionCode1
An Analysis and Mitigation of the Reversal CurseCode1
Adversarial score matching and improved sampling for image generationCode1
FLIGHT Mode On: A Feather-Light Network for Low-Light Image EnhancementCode1
Denoising Normalizing FlowCode1
Denoising Task Routing for Diffusion ModelsCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Denoising Lévy Probabilistic ModelsCode1
BARTpho: Pre-trained Sequence-to-Sequence Models for VietnameseCode1
FM2S: Towards Spatially-Correlated Noise Modeling in Zero-Shot Fluorescence Microscopy Image DenoisingCode1
Adversarial Schrödinger Bridge MatchingCode1
Denoising Masked AutoEncoders Help Robust ClassificationCode1
Denoising Implicit Feedback for RecommendationCode1
3D Vessel Graph Generation Using Denoising DiffusionCode1
Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude EstimationCode1
Denoising MCMC for Accelerating Diffusion-Based Generative ModelsCode1
From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image SegmentationCode1
Bilevel Fast Scene Adaptation for Low-Light Image EnhancementCode1
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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