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 5676–5700 of 7282 papers

TitleStatusHype
SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device—0
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information—0
An Effective Fusion Method to Enhance the Robustness of CNN—0
Class Information Guided Reconstruction for Automatic Modulation Open-Set Recognition—0
Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning—0
Class-specific image denoising using importance sampling—0
Class-specific Poisson denoising by patch-based importance sampling—0
An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture—0
Snapshot Compressed Imaging Based Single-Measurement Computer Vision for Videos—0
CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram—0
Snapshot HDR Video Construction Using Coded Mask—0
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning—0
client2vec: Towards Systematic Baselines for Banking Applications—0
Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network—0
SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition—0
SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation—0
CLoCKDistill: Consistent Location-and-Context-aware Knowledge Distillation for DETRs—0
SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning—0
Closed-Form Approximation of the Total Variation Proximal Operator—0
Closing the Gap: Domain Adaptation from Explicit to Implicit Discourse Relations—0
Cloud Removal With PolSAR-Optical Data Fusion Using A Two-Flow Residual Network—0
ClusterDDPM: An EM clustering framework with Denoising Diffusion Probabilistic Models—0
cMIM: A Contrastive Mutual Information Framework for Unified Generative and Discriminative Representation Learning—0
An Attention Free Conditional Autoencoder For Anomaly Detection in Cryptocurrencies—0
CNN-based TEM image denoising from first principles—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