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 2501–2550 of 7282 papers

TitleStatusHype
Improving Diffusion Models for Inverse Problems Using Optimal Posterior CovarianceCode2
Denoising Diffusion-Based Control of Nonlinear SystemsCode0
Decomposition-based and Interference Perception for Infrared and Visible Image Fusion in Complex Scenes—0
DiffVein: A Unified Diffusion Network for Finger Vein Segmentation and Authentication—0
Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization—0
Code Representation Learning At Scale—0
Cross-view Masked Diffusion Transformers for Person Image SynthesisCode2
LIR: A Lightweight Baseline for Image RestorationCode1
Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?—0
Plug-and-Play image restoration with Stochastic deNOising REgularizationCode1
Signal Quality Auditing for Time-series Data—0
AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video DataCode4
Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman SpectraCode0
Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models—0
AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction ErrorCode2
Diffusion Model Compression for Image-to-Image Translation—0
BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane ExtrapolationCode2
InstructIR: High-Quality Image Restoration Following Human InstructionsCode4
Sliced Wasserstein with Random-Path Projecting DirectionsCode0
Spatial-Aware Latent Initialization for Controllable Image Generation—0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization—0
Diffusion-based Graph Generative MethodsCode1
Data-Driven Estimation of the False Positive Rate of the Bayes Binary Classifier via Soft Labels—0
VJT: A Video Transformer on Joint Tasks of Deblurring, Low-light Enhancement and Denoising—0
Masked Pre-training Enables Universal Zero-shot DenoiserCode1
CascadedGaze: Efficiency in Global Context Extraction for Image RestorationCode2
Deconstructing Denoising Diffusion Models for Self-Supervised LearningCode2
Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle RecognitionCode1
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation LearningCode1
FLLIC: Functionally Lossless Image Compression—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
Graph Diffusion Transformers for Multi-Conditional Molecular GenerationCode2
UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion—0
Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet ExtractionCode1
Dual-Domain Coarse-to-Fine Progressive Estimation Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECTCode1
TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation—0
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation LearningCode0
Feature Denoising Diffusion Model for Blind Image Quality Assessment—0
Exploring Diffusion Time-steps for Unsupervised Representation LearningCode1
MotionMix: Weakly-Supervised Diffusion for Controllable Motion GenerationCode1
Product-Level Try-on: Characteristics-preserving Try-on with Realistic Clothes Shading and Wrinkles—0
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient—0
Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches—0
Large Language Models are Efficient Learners of Noise-Robust Speech RecognitionCode2
Application of Joint Notch Filtering and Wavelet Transform for Enhanced Powerline Interference Removal in Atrial Fibrillation Electrograms—0
Sub2Full: split spectrum to boost OCT despeckling without clean dataCode0
Fast graph-based denoising for point cloud color information—0
Automatic Tuning of Denoising Algorithms Parameters Without Ground TruthCode0
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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