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 3976–4000 of 7282 papers

TitleStatusHype
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising—0
Deep learning for full-field ultrasonic characterization—0
WIRE: Wavelet Implicit Neural RepresentationsCode1
Skip-Attention: Improving Vision Transformers by Paying Less Attention—0
PENDANTSS: PEnalized Norm-ratios Disentangling Additive Noise, Trend and Sparse SpikesCode0
Radio Frequency Fingerprints Extraction for LTE-V2X: A Channel Estimation Based Methodology—0
High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction—0
Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data SetsCode1
Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets—0
Task-specific Scene Structure RepresentationsCode1
Iterative Denoiser and Noise Estimator for Self-Supervised Image Denoising—0
Locating Noise is Halfway Denoising for Semi-Supervised Segmentation—0
Diffuse3D: Wide-Angle 3D Photography via Bilateral DiffusionCode1
HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models—0
Controllable Person Image Synthesis with Pose-Constrained Latent Diffusion—0
Feature Prediction Diffusion Model for Video Anomaly Detection—0
Fingerprinting Deep Image Restoration Models—0
A Latent Space of Stochastic Diffusion Models for Zero-Shot Image Editing and GuidanceCode1
Innovating Real Fisheye Image Correction with Dual Diffusion Architecture—0
CrossMatch: Source-Free Domain Adaptive Semantic Segmentation via Cross-Modal Consistency Training—0
Removing Anomalies as Noises for Industrial Defect Localization—0
PIRNet: Privacy-Preserving Image Restoration Network via Wavelet Lifting—0
Graphics2RAW: Mapping Computer Graphics Images to Sensor RAW Images—0
Noise2Info: Noisy Image to Information of Noise for Self-Supervised Image DenoisingCode0
Anomaly Detection using Score-based Perturbation ResilienceCode1
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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