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 4026–4050 of 7282 papers

TitleStatusHype
SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion ModelsCode0
Uncertainty Quantification via Neural Posterior Principal Components—0
Guided Frequency Loss for Image Restoration—0
Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation—0
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement—0
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning—0
Fully Adaptive Time-Varying Wave-Shape Model: Applications in Biomedical Signal ProcessingCode0
Wave-shape Function Model Order Estimation by Trigonometric Regression—0
An Ensemble Model for Distorted Images in Real Scenarios—0
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks—0
Image Denoising via Style Disentanglement—0
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models—0
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
PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement—0
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models—0
Self2Seg: Single-Image Self-Supervised Joint Segmentation and DenoisingCode0
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context—0
Reconstruct-and-Generate Diffusion Model for Detail-Preserving Image Denoising—0
Mixed Graph Signal Analysis of Joint Image Denoising / InterpolationCode0
Joint Demosaicing and Denoising with Double Deep Image Priors—0
Gradpaint: Gradient-Guided Inpainting with Diffusion 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