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 3201–3225 of 7282 papers

TitleStatusHype
Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model—0
Geophysical inverse problems with measurement-guided diffusion models—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
GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework—0
Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models—0
G-HOP: Generative Hand-Object Prior for Interaction Reconstruction and Grasp Synthesis—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
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
Equivariant plug-and-play image reconstruction—0
GLAMP: An Approximate Message Passing Framework for Transfer Learning with Applications to Lasso-based Estimators—0
Glauber Generative Model: Discrete Diffusion Models via Binary Classification—0
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation—0
Automatic Image Annotation via Label Transfer in the Semantic Space—0
Equivariant Denoisers for Image Restoration—0
Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks—0
Convergent regularization in inverse problems and linear plug-and-play denoisers—0
Guided Motion Diffusion for Controllable Human Motion Synthesis—0
ASD-Diffusion: Anomalous Sound Detection with Diffusion Models—0
EP-CFG: Energy-Preserving Classifier-Free Guidance—0
多樣訊雜比之訓練語料於降噪自動編碼器其語音強化功能之初步研究 (A Preliminary Study of Various SNR-level Training Data in the Denoising Auto-encoder (DAE) Technique for Speech Enhancement) [In Chinese]—0
Deep Interaction between Masking and Mapping Targets for Single-Channel Speech Enhancement—0
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models—0
GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—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