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 2101–2125 of 7282 papers

TitleStatusHype
TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking—0
Analytical Discovery of Manifold with Machine Learning—0
VIP: Video Inpainting Pipeline for Real World Human Removal—0
Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance—0
Enhancing LLM Robustness to Perturbed Instructions: An Empirical StudyCode0
MD-ProjTex: Texturing 3D Shapes with Multi-Diffusion Projection—0
Bridge the Gap between SNN and ANN for Image Restoration—0
BioAtt: Anatomical Prior Driven Low-Dose CT Denoising—0
Random Conditioning with Distillation for Data-Efficient Diffusion Model Compression—0
A Unified Approach to Analysis and Design of Denoising Markov Models—0
VideoScene: Distilling Video Diffusion Model to Generate 3D Scenes in One Step—0
Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation—0
Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-TrainingCode0
MixerMDM: Learnable Composition of Human Motion Diffusion Models—0
Denoising guarantees for optimized sampling schemes in compressed sensing—0
Deep Learning-Based Extended Target Tracking in ISAC Systems—0
OccludeNeRF: Geometric-aware 3D Scene Inpainting with Collaborative Score Distillation in NeRF—0
Foundation Models For Seismic Data Processing: An Extensive ReviewCode0
DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models—0
DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model—0
ExScene: Free-View 3D Scene Reconstruction with Gaussian Splatting from a Single Image—0
Enhancing Image Resolution of Solar Magnetograms: A Latent Diffusion Model ApproachCode0
SU-YOLO: Spiking Neural Network for Efficient Underwater Object DetectionCode0
Make Autoregressive Great Again: Diffusion-Free Graph Generation with Next-Scale Prediction—0
RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents—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