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

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Showing 5401–5450 of 7282 papers

TitleStatusHype
Adaptive Caching for Faster Video Generation with Diffusion Transformers—0
Weighted Schatten p-Norm Minimization for Image Denoising with Local and Nonlocal Regularization—0
OneActor: Consistent Character Generation via Cluster-Conditioned Guidance—0
One-dimensional Deep Image Prior for Time Series Inverse Problems—0
One Embedding To Do Them All—0
TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching—0
Weighted structure tensor total variation for image denoising—0
One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls—0
One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns—0
One-Pot Multi-Frame Denoising—0
One-shot Learning for Channel Estimation in Massive MIMO Systems—0
Prior-Guided Diffusion Planning for Offline Reinforcement Learning—0
One Signal-Noise Separation based Wiener Filter for Magnetogastrogram—0
One Size Fits All: Can We Train One Denoiser for All Noise Levels?—0
Tuning-Free Inversion-Enhanced Control for Consistent Image Editing—0
Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion—0
One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation—0
Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models—0
Adapting Sentence Transformers for the Aviation Domain—0
On Exact Inversion of DPM-Solvers—0
On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models—0
On Grid Compressive Sampling for Spherical Field Measurements in Acoustics—0
On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstruction—0
Online Adaptation of Deep Architectures with Reinforcement Learning—0
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
TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopy—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