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 4476–4500 of 7282 papers

TitleStatusHype
InterAnimate: Taming Region-aware Diffusion Model for Realistic Human Interaction Animation—0
Inter-Diffusion Generation Model of Speakers and Listeners for Effective Communication—0
Interferometric Passive Radar Imaging with Deep Denoising Priors—0
InterLCM: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration—0
Interleaved Gibbs Diffusion for Constrained Generation—0
The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional recurrent Network for Multi Channel Speech Enhancement and Speech Recognition—0
The potential of self-supervised networks for random noise suppression in seismic data—0
Interpolation and Denoising of Seismic Data using Convolutional Neural Networks—0
Interpretable and robust blind image denoising with bias-free convolutional neural networks—0
Interpretable Deep Learning Paradigm for Airborne Transient Electromagnetic Inversion—0
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs—0
Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing—0
Interpretable Stability Bounds for Spectral Graph Filters—0
The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling—0
The probability flow ODE is provably fast—0
The Pseudo Projection Operator: Applications of Deep Learning to Projection Based Filtering in Non-Trivial Frequency Regimes—0
Interpreting What Typical Fault Signals Look Like via Prototype-matching—0
Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization—0
Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models—0
Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation—0
Inverse Flow and Consistency Models—0
A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines—0
InverseNet: Solving Inverse Problems with Splitting Networks—0
Thermal Face Image Classification using Deep Learning Techniques—0
Inverse problems with second-order Total Generalized Variation constraints—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