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 2351–2375 of 7282 papers

TitleStatusHype
Improved Diffusion-based Generative Model with Better Adversarial RobustnessCode0
Adversarial Regularizers in Inverse ProblemsCode0
Contextual Checkerboard Denoise -- A Novel Neural Network-Based Approach for Classification-Aware OCT Image DenoisingCode0
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular DynamicsCode0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
Context-aware Deep Feature Compression for High-speed Visual TrackingCode0
Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomyCode0
Implicit 3D Orientation Learning for 6D Object Detection from RGB ImagesCode0
ImPoster: Text and Frequency Guidance for Subject Driven Action Personalization using Diffusion ModelsCode0
Isotropic and Steerable Wavelets in N Dimensions. A multiresolution analysis framework for ITKCode0
Mesh Denoising with Facet Graph ConvolutionsCode0
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip ConnectionsCode0
Image Restoration using Plug-and-Play CNN MAP DenoisersCode0
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Image Restoration Using Deep Regulated Convolutional NetworksCode0
Constrained and unconstrained deep image prior optimization models with automatic regularizationCode0
Image Reconstruction with Predictive Filter FlowCode0
Consistent Human Image and Video Generation with Spatially Conditioned DiffusionCode0
Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred LearningCode0
Consistent Autoformalization for Constructing Mathematical LibrariesCode0
Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparisonCode0
ConsistencyTrack: A Robust Multi-Object Tracker with a Generation Strategy of Consistency ModelCode0
Consistency Models for Scalable and Fast Simulation-Based InferenceCode0
Image Inpainting via Tractable Steering of Diffusion ModelsCode0
Image quality measurements and denoising using Fourier Ring CorrelationsCode0
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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