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 5601–5625 of 7282 papers

TitleStatusHype
Efficient randomized smoothing by denoising with learned score function—0
Self-supervised Bayesian Deep Learning for Image Denoising—0
Frequency Regularized Deep Convolutional Dictionary Learning and Application to Blind Denoising—0
A Simple Sparse Denoising Layer for Robust Deep Learning—0
Three-quarter Sibling Regression for Denoising Observational Data—0
Conditional Generation of Temporally-ordered Event Sequences—0
Promoting Graph Awareness in Linearized Graph-to-Text Generation—0
Revisiting Robust Neural Machine Translation: A Transformer Case Study—0
Denoising quantum states with Quantum Autoencoders -- Theory and ApplicationsCode0
A Plug-and-Play Priors Framework for Hyperspectral UnmixingCode0
Towards Boosting the Channel Attention in Real Image Denoising : Sub-band Pyramid Attention—0
HDR Denoising and Deblurring by Learning Spatio-temporal Distortion Models—0
Evolutionary Variational Optimization of Generative Models—0
Improving J-divergence of brain connectivity states by graph Laplacian denoising—0
Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift AlgorithmCode0
Object Detection based on OcSaFPN in Aerial Images with Noise—0
A Survey on the Visual Perceptions of Gaussian Noise Filtering on Photography—0
Denoising Text to Speech with Frame-Level Noise Modeling—0
On the Limitations of Denoising Strategies as Adversarial Defenses—0
Relightable 3D Head Portraits from a Smartphone Video—0
Revisit 1D Total Variation restoration problem with new real-time algorithms for signal and hyper-parameter estimationsCode0
Projected Distribution Loss for Image EnhancementCode0
TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-Resolution Processing of Atomic-Resolution Images—0
Mesh Denoising with Facet Graph ConvolutionsCode0
On Low-Rank Hankel Matrix Denoising—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