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 4301–4325 of 7282 papers

TitleStatusHype
Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models—0
Online and Stable Learning of Analysis Operators—0
Tuning-Free Noise Rectification for High Fidelity Image-to-Video Generation—0
Online Deep Learning based on Auto-Encoder—0
Online Filtering over Expanding Graphs—0
Online Joint Topology Identification and Signal Estimation from Streams with Missing Data—0
Online Nonnegative Matrix Factorization with General Divergences—0
Online Nonnegative Matrix Factorization with Outliers—0
Online Regularization by Denoising with Applications to Phase Retrieval—0
Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and Denoising Autoencoders—0
On Low-Rank Hankel Matrix Denoising—0
On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent—0
Weight Encode Reconstruction Network for Computed Tomography in a Semi-Case-Wise and Learning-Based Way—0
On Memory Construction and Retrieval for Personalized Conversational Agents—0
On Nearest Neighbors in Non Local Means Denoising—0
On normalization-equivariance properties of supervised and unsupervised denoising methods: a survey—0
On reconstruction algorithms for signals sparse in Hermite and Fourier domains—0
On Stacked Denoising Autoencoder based Pre-training of ANN for Isolated Handwritten Bengali Numerals Dataset Recognition—0
On The Application Of Log Compression and Enhanced Denoising In Contrast Enhancement Of Digital Radiography Images—0
Tuning Frequency Bias of State Space Models—0
On the Behavior of Intrusive and Non-intrusive Speech Enhancement Metrics in Predictive and Generative Settings—0
Turbocharging Fluid Antenna Multiple Access—0
On the Condition Monitoring of Bolted Joints through Acoustic Emission and Deep Transfer Learning: Generalization, Ordinal Loss and Super-Convergence—0
On the Connection Between Diffusion Models and Molecular Dynamics—0
On the convergence of the IRLS algorithm in Non-Local Patch Regression—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