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 6201–6225 of 7282 papers

TitleStatusHype
Speech Denoising Convolutional Neural Network trained with Deep Feature Losses.Code0
Dataset Distillers Are Good Label Denoisers In the WildCode0
Denoising autoencoder with modulated lateral connections learns invariant representations of natural imagesCode0
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete DiffusionCode0
Adaptive Long-term Embedding with Denoising and Augmentation for RecommendationCode0
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational InferenceCode0
Beyond Human Perception: Understanding Multi-Object World from Monocular ViewCode0
Denoising-based Contractive Imitation LearningCode0
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue ConditionCode0
Vision-guided and Mask-enhanced Adaptive Denoising for Prompt-based Image EditingCode0
Isotropic and Steerable Wavelets in N Dimensions. A multiresolution analysis framework for ITKCode0
Untrained Graph Neural Networks for DenoisingCode0
Self2Seg: Single-Image Self-Supervised Joint Segmentation and DenoisingCode0
Iterative Camera-LiDAR Extrinsic Optimization via Surrogate DiffusionCode0
Denoising Bottleneck with Mutual Information Maximization for Video Multimodal FusionCode0
A Review of Convolutional Neural Networks for Inverse Problems in ImagingCode0
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence PretrainingCode0
Natural Image Noise DatasetCode0
Denoising Deep Generative ModelsCode0
Deep Learning based Switching Filter for Impulsive Noise Removal in Color ImagesCode0
Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision LocalizationCode0
The Spectral Bias of the Deep Image PriorCode0
FEUNet: a flexible and effective U-shaped network for image denoisingCode0
Denoising Diffusion-Based Control of Nonlinear SystemsCode0
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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