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 4026–4050 of 7282 papers

TitleStatusHype
Adversarial Feature Learning and Unsupervised Clustering based Speech Synthesis for Found Data with Acoustic and Textual Noise—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
GenzIQA: Generalized Image Quality Assessment using Prompt-Guided Latent Diffusion Models—0
TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis—0
Geodesic Gramian Denoising Applied to the Images Contaminated With Noise Sampled From Diverse Probability Distributions—0
GeoDirDock: Guiding Docking Along Geodesic Paths—0
GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoising—0
Geometric and Learning-based Mesh Denoising: A Comprehensive Survey—0
Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes—0
Geometric Machine Learning on EEG Signals—0
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction—0
Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection—0
Geophysical inverse problems with measurement-guided diffusion models—0
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining—0
Adversarial Defense via Image Denoising with Chaotic Encryption—0
GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework—0
VJT: A Video Transformer on Joint Tasks of Deblurring, Low-light Enhancement and Denoising—0
G-HOP: Generative Hand-Object Prior for Interaction Reconstruction and Grasp Synthesis—0
Tell Me What You See: Text-Guided Real-World Image Denoising—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
Adversarial Contrastive Domain-Generative Learning for Bacteria Raman Spectrum Joint Denoising and Cross-Domain Identification—0
GLAMP: An Approximate Message Passing Framework for Transfer Learning with Applications to Lasso-based Estimators—0
Glauber Generative Model: Discrete Diffusion Models via Binary Classification—0
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation—0
Global hard thresholding algorithms for joint sparse image representation and denoising—0
Show:102550
← PrevPage 162 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