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 3176–3200 of 7282 papers

TitleStatusHype
Generative Speech Foundation Model Pretraining for High-Quality Speech Extraction and Restoration—0
Generative thermodynamic computing—0
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction—0
HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging—0
GenesisTex: Adapting Image Denoising Diffusion to Texture Space—0
HOIAnimator: Generating Text-prompt Human-object Animations using Novel Perceptive Diffusion Models—0
Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model—0
Application of Spherical Convolutional Neural Networks to Image Reconstruction and Denoising in Nuclear Medicine—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models—0
GenzIQA: Generalized Image Quality Assessment using Prompt-Guided Latent Diffusion Models—0
A Second Order Cumulant Spectrum Test That a Stochastic Process is Strictly Stationary and a Step Toward a Test for Graph Signal Strict Stationarity—0
Equivariant plug-and-play image reconstruction—0
Geodesic Gramian Denoising Applied to the Images Contaminated With Noise Sampled From Diverse Probability Distributions—0
GeoDirDock: Guiding Docking Along Geodesic Paths—0
Equivariant Denoisers for Image Restoration—0
Convergent regularization in inverse problems and linear plug-and-play denoisers—0
Deep Gaussian Conditional Random Field Network: A Model-based Deep Network for Discriminative Denoising—0
Improved Techniques for Adversarial Discriminative Domain Adaptation—0
Geometric and Learning-based Mesh Denoising: A Comprehensive Survey—0
Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes—0
Deep Generative Models for 3D Medical Image Synthesis—0
Geometric Machine Learning on EEG Signals—0
ASD-Diffusion: Anomalous Sound Detection with Diffusion Models—0
EP-CFG: Energy-Preserving Classifier-Free Guidance—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