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 1651–1700 of 7282 papers

TitleStatusHype
PFDiff: Training-free Acceleration of Diffusion Models through the Gradient Guidance of Past and FutureCode1
Modeling the Neonatal Brain Development Using Implicit Neural RepresentationsCode0
A lifted Bregman strategy for training unfolded proximal neural network Gaussian denoisers—0
Classifier-Free Guidance is a Predictor-Corrector—0
Cross-Modal Denoising: A Novel Training Paradigm for Enhancing Speech-Image Retrieval—0
Learned denoising with simulated and experimental low-dose CT data—0
Deep Joint Denoising and Detection for Enhanced Intracellular Particle Analysis—0
LLM4DSR: Leveraing Large Language Model for Denoising Sequential Recommendation—0
Disentangle and denoise: Tackling context misalignment for video moment retrieval—0
Multi-task Heterogeneous Graph Learning on Electronic Health RecordsCode1
Joint Graph Rewiring and Feature Denoising via Spectral ResonanceCode1
BSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech Enhancement Network based on Self-Supervised EmbeddingCode0
How to Best Combine Demosaicing and Denoising?—0
Low-Bitwidth Floating Point Quantization for Efficient High-Quality Diffusion Models—0
Image Denoising Using Green Channel PriorCode1
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information—0
Strong denoising of financial time-series—0
TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature ScalingCode1
Efficient Diffusion Transformer with Step-wise Dynamic Attention MediatorsCode1
U-DECN: End-to-End Underwater Object Detection ConvNet with Improved DeNoising TrainingCode0
Evaluating BM3D and NBNet: A Comprehensive Study of Image Denoising Across Multiple Datasets—0
MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent ImputationCode1
Pretrained-Guided Conditional Diffusion Models for Microbiome Data AnalysisCode1
ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack—0
Masked Graph Autoencoders with Contrastive Augmentation for Spatially Resolved Transcriptomics DataCode1
Adaptive Cohen's Class Time-Frequency Distribution—0
Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology—0
Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches—0
Lightweight Video Denoising Using a Classic Bayesian Backbone—0
Bayes-optimal learning of an extensive-width neural network from quadratically many samplesCode0
Learning from Noisy Labels for Long-tailed Data via Optimal Transport—0
L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object DetectionCode2
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models—0
TALE: Training-free Cross-domain Image Composition via Adaptive Latent Manipulation and Energy-guided Optimization—0
Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models—0
Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey—0
Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred LearningCode0
MDT-A2G: Exploring Masked Diffusion Transformers for Co-Speech Gesture Generation—0
Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models—0
Cascading Refinement Video Denoising with Uncertainty Adaptivity—0
A Two-Stage Progressive Pre-training using Multi-Modal Contrastive Masked Autoencoders—0
On Probabilistic Embeddings in Optimal Dimension Reduction—0
Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models—0
LDFaceNet: Latent Diffusion-based Network for High-Fidelity Deepfake Generation—0
Step Saver: Predicting Minimum Denoising Steps for Diffusion Model Image Generation—0
Landmark-guided Diffusion Model for High-fidelity and Temporally Coherent Talking Head Generation—0
TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling—0
CLIP4Sketch: Enhancing Sketch to Mugshot Matching through Dataset Augmentation using Diffusion Models—0
Stop-and-go wave super-resolution reconstruction via iterative refinement—0
Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection—0
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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