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 2951–2975 of 7282 papers

TitleStatusHype
Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE—0
M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment AnalysisCode1
MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion—0
DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction—0
Denoising Opponents Position in Partial Observation Environment—0
A Coordinate Descent Approach to Atomic Norm Denoising—0
EDGE++: Improved Training and Sampling of EDGE—0
Diffusion-Based Adversarial Purification for Speaker Verification—0
NMR Spectra Denoising with Vandermonde Constraints—0
Code-Switching with Word Senses for Pretraining in Neural Machine Translation—0
Composer Style-specific Symbolic Music Generation Using Vector Quantized Discrete Diffusion ModelsCode1
TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models—0
Auxiliary Features-Guided Super Resolution for Monte Carlo Rendering—0
Music Augmentation and Denoising For Peak-Based Audio FingerprintingCode1
Product of Gaussian Mixture Diffusion ModelsCode0
Conditional Generative Modeling for Images, 3D Animations, and Video—0
Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning—0
LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising—0
Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise DetectionCode0
Object-aware Inversion and Reassembly for Image EditingCode1
PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image DenoisingCode1
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework DesignCode1
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys—0
Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal CellsCode0
Provable Probabilistic Imaging using Score-Based Generative PriorsCode1
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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