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 6451–6475 of 7282 papers

TitleStatusHype
Differentiable Surface Splatting for Point-based Geometry ProcessingCode0
Lateral Connections in Denoising Autoencoders Support Supervised LearningCode0
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and DecodingCode0
SoundSil-DS: Deep Denoising and Segmentation of Sound-field Images with SilhouettesCode0
Bivariate Matrix-valued Linear Regression (BMLR): Finite-sample performance under Identifiability and Sparsity AssumptionsCode0
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured DataCode0
Self-Guided Network for Fast Image DenoisingCode0
A Simple Yet Effective SVD-GCN for Directed GraphsCode0
Layered Rendering Diffusion Model for Controllable Zero-Shot Image SynthesisCode0
Deep Perceptual Enhancement for Medical Image AnalysisCode0
Generalization through variance: how noise shapes inductive biases in diffusion modelsCode0
When Image Denoising Meets High-Level Vision Tasks: A Deep Learning ApproachCode0
Generalized Deep Image to Image RegressionCode0
Generalized Denoising Auto-Encoders as Generative ModelsCode0
Connecting Image Denoising and High-Level Vision Tasks via Deep LearningCode0
Deep Burst DenoisingCode0
DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic ModelCode0
Generalized Laplacian Regularized Framelet Graph Neural NetworksCode0
Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial IterationsCode0
Physical spline for denoising object trajectory data by combining splines, ML feature regression and model knowledgeCode0
Generalized Octave Convolutions for Learned Multi-Frequency Image CompressionCode0
SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade SensorsCode0
Using Galaxy Evolution as Source of Physics-Based Ground Truth for Generative ModelsCode0
Generalized Robust Fundus Photography-based Vision Loss Estimation for High MyopiaCode0
TrIND: Representing Anatomical Trees by Denoising Diffusion of Implicit Neural FieldsCode0
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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