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 3826–3850 of 7282 papers

TitleStatusHype
ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learning—0
Equivariant plug-and-play image reconstruction—0
X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model—0
Slice3D: Multi-Slice, Occlusion-Revealing, Single View 3D Reconstruction—0
AAMDM: Accelerated Auto-regressive Motion Diffusion Model—0
LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models—0
Layered Rendering Diffusion Model for Controllable Zero-Shot Image SynthesisCode0
On Exact Inversion of DPM-Solvers—0
Diffusion Models Without Attention—0
HiFi Tuner: High-Fidelity Subject-Driven Fine-Tuning for Diffusion Models—0
Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware LearningCode0
Leveraging Graph Diffusion Models for Network Refinement Tasks—0
Improving Interpretation Faithfulness for Vision Transformers—0
Spice-E : Structural Priors in 3D Diffusion using Cross-Entity Attention—0
Using Ornstein-Uhlenbeck Process to understand Denoising Diffusion Probabilistic Model and its Noise Schedules—0
Image Inpainting via Tractable Steering of Diffusion ModelsCode0
Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insightCode0
Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain—0
LC4SV: A Denoising Framework Learning to Compensate for Unseen Speaker Verification Models—0
Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization—0
Exploring Attribute Variations in Style-based GANs using Diffusion Models—0
Bayesian Formulations for Graph Spectral Denoising—0
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations—0
One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls—0
Self-supervised OCT Image Denoising with Slice-to-Slice Registration and ReconstructionCode0
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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