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 4601–4625 of 7282 papers

TitleStatusHype
Tensor Denoising via Amplification and Stable Rank Methods—0
Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models—0
CLASH: Contrastive learning through alignment shifting to extract stimulus information from EEG—0
Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --—0
Deep learning for full-field ultrasonic characterization—0
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising—0
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
Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets—0
Iterative Denoiser and Noise Estimator for Self-Supervised Image Denoising—0
Feature Prediction Diffusion Model for Video Anomaly Detection—0
Graphics2RAW: Mapping Computer Graphics Images to Sensor RAW Images—0
PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction—0
Removing Anomalies as Noises for Industrial Defect Localization—0
Diffusion-Based Signed Distance Fields for 3D Shape Generation—0
Controllable Person Image Synthesis with Pose-Constrained Latent Diffusion—0
HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models—0
PIRNet: Privacy-Preserving Image Restoration Network via Wavelet Lifting—0
Noise2Info: Noisy Image to Information of Noise for Self-Supervised Image DenoisingCode0
Innovating Real Fisheye Image Correction with Dual Diffusion Architecture—0
CrossMatch: Source-Free Domain Adaptive Semantic Segmentation via Cross-Modal Consistency Training—0
Locating Noise is Halfway Denoising for Semi-Supervised Segmentation—0
Fingerprinting Deep Image Restoration Models—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