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 1651–1675 of 7282 papers

TitleStatusHype
PFDiff: Training-free Acceleration of Diffusion Models through the Gradient Guidance of Past and FutureCode1
MicroSSIM: Improved Structural Similarity for Comparing Microscopy DataCode0
Modeling the Neonatal Brain Development Using Implicit Neural RepresentationsCode0
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
Multi-task Heterogeneous Graph Learning on Electronic Health RecordsCode1
Disentangle and denoise: Tackling context misalignment for video moment retrieval—0
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
Low-Bitwidth Floating Point Quantization for Efficient High-Quality Diffusion Models—0
How to Best Combine Demosaicing and Denoising?—0
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information—0
Image Denoising Using Green Channel PriorCode1
Strong denoising of financial time-series—0
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
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
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
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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