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 1226–1250 of 7282 papers

TitleStatusHype
Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise ModelingCode1
Denoising Normalizing FlowCode1
Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidenceCode1
Learning an Adaptive Model for Extreme Low-light Raw Image ProcessingCode1
Conditional Consistency Guided Image Translation and EnhancementCode1
Conditional Controllable Image FusionCode1
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation LearningCode1
Learning low-rank latent mesoscale structures in networksCode1
Denoising Relation Extraction from Document-level Distant SupervisionCode1
Denoising Task Routing for Diffusion ModelsCode1
Conditional Diffusion Models for Weakly Supervised Medical Image SegmentationCode1
Learning Robust Recommender from Noisy Implicit FeedbackCode1
DDM^2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion ModelsCode1
IDR: Self-Supervised Image Denoising via Iterative Data RefinementCode1
Illuminating Darkness: Enhancing Real-world Low-light Scenes with Smartphone ImagesCode1
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion DelineationCode1
Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned GuidanceCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at ScaleCode1
Conditional score-based diffusion models for Bayesian inference in infinite dimensionsCode1
Conditional Simulation Using Diffusion Schrödinger BridgesCode1
Hyperspectral Image Denoising Using SURE-Based Unsupervised Convolutional Neural NetworksCode1
Classifier-free Guidance with Adaptive ScalingCode1
3D Shape Generation and Completion through Point-Voxel DiffusionCode1
3D molecule generation by denoising voxel gridsCode1
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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