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 7001–7025 of 7282 papers

TitleStatusHype
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) - The _0 Method—0
Principal Basis Analysis in Sparse Representation—0
Performance Limits of Stochastic Sub-Gradient Learning, Part I: Single Agent Case—0
Cascading Denoising Auto-Encoder as a Deep Directed Generative Model—0
Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and Denoising Autoencoders—0
Screen Content Image Segmentation Using Sparse-Smooth Decomposition—0
Acceleration of the PDHGM on strongly convex subspaces—0
Binding via Reconstruction ClusteringCode0
Multimodal sparse representation learning and applications—0
Denoising Criterion for Variational Auto-Encoding Framework—0
Deconstructing the Ladder Network Architecture—0
FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning and Applications—0
Predicting online user behaviour using deep learning algorithms—0
Why are deep nets reversible: A simple theory, with implications for training—0
Predicting distributions with Linearizing Belief NetworksCode0
On the interplay of network structure and gradient convergence in deep learning—0
Graph-based denoising for time-varying point clouds—0
Learning Representations of Affect from Speech—0
Reversible Recursive Instance-level Object Segmentation—0
Deep Gaussian Conditional Random Field Network: A Model-based Deep Network for Discriminative Denoising—0
LLNet: A Deep Autoencoder Approach to Natural Low-light Image EnhancementCode0
Hyperspectral Image Recovery via Hybrid Regularization—0
Poisson Inverse Problems by the Plug-and-Play scheme—0
Enhanced Low-Rank Matrix Approximation—0
A note on the evaluation of generative modelsCode0
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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