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 2876–2900 of 7282 papers

TitleStatusHype
Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation—0
A Deep Learning Method for Simultaneous Denoising and Missing Wedge Reconstruction in Cryogenic Electron TomographyCode1
Improved DDIM Sampling with Moment Matching Gaussian Mixtures—0
Weakly-supervised deepfake localization in diffusion-generated imagesCode1
LuminanceL1Loss: A loss function which measures percieved brightness and colour differences—0
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising ApproachCode0
DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET ImagingCode0
Reducing Spatial Fitting Error in Distillation of Denoising Diffusion ModelsCode0
3DifFusionDet: Diffusion Model for 3D Object Detection with Robust LiDAR-Camera Fusion—0
Image Restoration via Frequency SelectionCode1
Cross-Image Attention for Zero-Shot Appearance Transfer—0
Zero-Shot Enhancement of Low-Light Image Based on Retinex DecompositionCode0
Robust Generalization Strategies for Morpheme Glossing in an Endangered Language Documentation Context—0
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis—0
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot ClassificationCode1
Thermal Face Image Classification using Deep Learning Techniques—0
Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising ModelCode0
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation—0
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method—0
Sparse Training of Discrete Diffusion Models for Graph GenerationCode1
Quantum circuit synthesis with diffusion modelsCode1
CDGraph: Dual Conditional Social Graph Synthesizing via Diffusion Model—0
Combating Bilateral Edge Noise for Robust Link PredictionCode1
Add and Thin: Diffusion for Temporal Point Processes—0
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models—0
Show:102550
← PrevPage 116 of 292Next →
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