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 701–725 of 7282 papers

TitleStatusHype
Interleaved Gibbs Diffusion for Constrained Generation—0
Denoising Designs-inherited Search Framework for Image Denoising—0
Generalization error bound for denoising score matching under relaxed manifold assumption—0
GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects—0
Is Noise Conditioning Necessary for Denoising Generative Models?—0
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding—0
Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific Data Compression—0
Inverse Flow and Consistency Models—0
Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory SharpeningCode2
Exploiting network optimization stability for enhanced PET image denoising using deep image prior—0
CLoCKDistill: Consistent Location-and-Context-aware Knowledge Distillation for DETRs—0
NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids—0
Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion—0
Self-Consistent Model-based Adaptation for Visual Reinforcement Learning—0
Compression-Aware One-Step Diffusion Model for JPEG Artifact RemovalCode2
Classifier-free Guidance with Adaptive ScalingCode1
OptimOTU: Taxonomically aware OTU clustering with optimized thresholds and a bioinformatics workflow for metabarcoding data—0
Residual Transformer Fusion Network for Salt and Pepper Image Denoising—0
Unleashing the Power of Large Language Model for Denoising Recommendation—0
E-MD3C: Taming Masked Diffusion Transformers for Efficient Zero-Shot Object Customization—0
Quaternion-Hadamard Network: A Novel Defense Against Adversarial Attacks with a New Dataset—0
Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation—0
SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generation—0
Concentration Inequalities for the Stochastic Optimization of Unbounded Objectives with Application to Denoising Score Matching—0
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy—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