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 3876–3900 of 7282 papers

TitleStatusHype
Low Latency Video Denoising for Online Conferencing Using CNN Architectures—0
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule GenerationCode1
Adversarial Contrastive Distillation with Adaptive Denoising—0
Boundary Guided Learning-Free Semantic Control with Diffusion ModelsCode1
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial OptimizationCode2
DeepSpace: Dynamic Spatial and Source Cue Based Source Separation for Dialog Enhancement—0
Speech Enhancement with Multi-granularity Vector Quantization—0
Explicit Diffusion of Gaussian Mixture Model Based Image Priors—0
Filtered Iterative Denoising for Linear Inverse Problems—0
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution—0
DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion ModelsCode1
Score-based Diffusion Models in Function Space—0
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action—0
Robust Unsupervised StyleGAN Image RestorationCode1
Deep Transfer Tensor Factorization for Multi-View Learning—0
Denoising and Prompt-Tuning for Multi-Behavior RecommendationCode1
Single Motion DiffusionCode2
Metaphor Detection with Effective Context DenoisingCode0
Star-Shaped Denoising Diffusion Probabilistic ModelsCode1
Language-Aware Multilingual Machine Translation with Self-Supervised LearningCode0
A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocksCode0
UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion ModelsCode2
Better Diffusion Models Further Improve Adversarial TrainingCode1
MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model—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