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 2701–2725 of 7282 papers

TitleStatusHype
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment—0
LatentEditor: Text Driven Local Editing of 3D ScenesCode1
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design—0
LIME: Localized Image Editing via Attention Regularization in Diffusion Models—0
Reconstruction of Sound Field through Diffusion Models—0
Session-Based Recommendation by Exploiting Substitutable and Complementary Relationships from Multi-behavior Data—0
The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven InitializationCode1
World Models via Policy-Guided Trajectory DiffusionCode1
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise ToleranceCode1
Denoising diffusion-based synthetic generation of three-dimensional (3D) anisotropic microstructures from two-dimensional (2D) micrographs—0
SPD-DDPM: Denoising Diffusion Probabilistic Models in the Symmetric Positive Definite SpaceCode0
LMD: Faster Image Reconstruction with Latent Masking DiffusionCode0
SimAC: A Simple Anti-Customization Method for Protecting Face Privacy against Text-to-Image Synthesis of Diffusion ModelsCode1
ρ-Diffusion: A diffusion-based density estimation framework for computational physicsCode0
Image is All You Need to Empower Large-scale Diffusion Models for In-Domain GenerationCode1
Time Series Diffusion Method: A Denoising Diffusion Probabilistic Model for Vibration Signal Generation—0
ClusterDDPM: An EM clustering framework with Denoising Diffusion Probabilistic Models—0
Clockwork Diffusion: Efficient Generation With Model-Step DistillationCode1
Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements—0
A New Perspective On Denoising Based On Optimal Transport—0
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-ExpertsCode1
Hyper-Restormer: A General Hyperspectral Image Restoration Transformer for Remote Sensing Imaging—0
LoRA-Enhanced Distillation on Guided 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