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

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Showing 6451–6500 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
Massive Styles Transfer with Limited Labeled DataCode0
Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource Parallel DataCode0
DiffMotion: Speech-Driven Gesture Synthesis Using Denoising Diffusion ModelCode0
When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising RecommendationCode0
DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech TranslationCode0
Learned Convolutional Sparse CodingCode0
TC-PDM: Temporally Consistent Patch Diffusion Models for Infrared-to-Visible Video TranslationCode0
Learned D-AMP: Principled Neural Network based Compressive Image RecoveryCode0
DiffPAD: Denoising Diffusion-based Adversarial Patch DecontaminationCode0
Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot LearningCode0
Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT AngiographyCode0
Source Camera Identification with Multi-Scale Feature Fusion NetworkCode0
Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix ApproximationCode0
Deep Hyperspectral Prior: Denoising, Inpainting, Super-ResolutionCode0
Self-Supervised Deep Depth DenoisingCode0
High-Quality Self-Supervised Deep Image DenoisingCode0
Deep Perceptual Enhancement for Medical Image AnalysisCode0
Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image Classification TasksCode0
STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image DenoisingCode0
Times series averaging and denoising from a probabilistic perspective on time-elastic kernelsCode0
Generating observation guided ensembles for data assimilation with denoising diffusion probabilistic modelCode0
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-DecodingCode0
Generating symbolic music using diffusion modelsCode0
Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware LearningCode0
Residual Dense Network for Image RestorationCode0
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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