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 2001–2025 of 7282 papers

TitleStatusHype
Denoising Fisher Training For Neural Implicit Samplers—0
Denoising Fast X-Ray Fluorescence Raster Scans of Paintings—0
DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restoration—0
BJTU-WeChat's Systems for the WMT22 Chat Translation Task—0
DiffBrush:Just Painting the Art by Your Hands—0
DiffCAP: Diffusion-based Cumulative Adversarial Purification for Vision Language Models—0
Diff-CAPTCHA: An Image-based CAPTCHA with Security Enhanced by Denoising Diffusion Model—0
An algorithm for improving Non-Local Means operators via low-rank approximation—0
Denoising ESG: quantifying data uncertainty from missing data with Machine Learning and prediction intervals—0
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Denoising ECG by Adaptive Filter with Empirical Mode Decomposition—0
Bitwise Operations of Cellular Automaton on Gray-scale Images—0
DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Models—0
3DDesigner: Towards Photorealistic 3D Object Generation and Editing with Text-guided Diffusion Models—0
Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models—0
Dissipative residual layers for unsupervised implicit parameterization of data manifolds—0
Denoising Distant Supervision for Relation Extraction via Instance-Level Adversarial Training—0
Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising—0
Denoising Diffusion via Image-Based Rendering—0
An Adaptive Parameter Estimation for Guided Filter based Image Deconvolution—0
Differentiable Programming for Piecewise Polynomial Functions—0
Discriminative Transfer Learning for General Image Restoration—0
Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity—0
Denoising Diffusion Semantic Segmentation with Mask Prior Modeling—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