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 3051–3075 of 7282 papers

TitleStatusHype
Object Motion Guided Human Motion Synthesis—0
Bayesian Cramér-Rao Bound Estimation with Score-Based Models—0
DeepPCR: Parallelizing Sequential Operations in Neural Networks—0
Neuromorphic Imaging with Joint Image Deblurring and Event Denoising—0
Distilling ODE Solvers of Diffusion Models into Smaller Steps—0
Guided Frequency Loss for Image Restoration—0
High Perceptual Quality Wireless Image Delivery with Denoising Diffusion ModelsCode1
Uncertainty Quantification via Neural Posterior Principal Components—0
Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation—0
Fully Adaptive Time-Varying Wave-Shape Model: Applications in Biomedical Signal ProcessingCode0
Wave-shape Function Model Order Estimation by Trigonometric Regression—0
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning—0
An Ensemble Model for Distorted Images in Real Scenarios—0
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement—0
Image Denoising via Style Disentanglement—0
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks—0
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models—0
On the Posterior Distribution in Denoising: Application to Uncertainty QuantificationCode1
Connecting Image Inpainting with Denoising in the Homogeneous Diffusion Setting—0
DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesis—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
Light Field Diffusion for Single-View Novel View Synthesis—0
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models—0
FreeU: Free Lunch in Diffusion U-NetCode3
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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