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 3751–3775 of 7282 papers

TitleStatusHype
M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising—0
Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective—0
YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency—0
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction—0
Machine Learning-Based Automated Assessment of Intracorporeal Suturing in Laparoscopic Fundoplication—0
Machine Learning Enhances Algorithms for Quantifying Non-Equilibrium Dynamics in Correlation Spectroscopy Experiments to Reach Frame-Rate-Limited Time Resolution—0
Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds (Preliminary Draft)—0
Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds—0
Machine Learning Techniques and Applications For Ground-based Image Analysis—0
Can Active Label Correction Improve LLM-based Modular AI Systems?—0
Towards Biologically Plausible Deep Learning—0
MADiff: Motion-Aware Mamba Diffusion Models for Hand Trajectory Prediction on Egocentric Videos—0
MadSGM: Multivariate Anomaly Detection with Score-based Generative Models—0
MagicComp: Training-free Dual-Phase Refinement for Compositional Video Generation—0
MagicInfinite: Generating Infinite Talking Videos with Your Words and Voice—0
Towards Boosting the Channel Attention in Real Image Denoising : Sub-band Pyramid Attention—0
Magic Mushroom: A Customizable Benchmark for Fine-grained Analysis of Retrieval Noise Erosion in RAG Systems—0
MagicScroll: Nontypical Aspect-Ratio Image Generation for Visual Storytelling via Multi-Layered Semantic-Aware Denoising—0
MagicStyle: Portrait Stylization Based on Reference Image—0
Additive Component Analysis—0
Add and Thin: Diffusion for Temporal Point Processes—0
Majorization Minimization Technique for Optimally Solving Deep Dictionary Learning—0
Towards Chunk-Wise Generation for Long Videos—0
Make-A-Protagonist: Generic Video Editing with An Ensemble of Experts—0
Make Autoregressive Great Again: Diffusion-Free Graph Generation with Next-Scale Prediction—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