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 2251–2275 of 7282 papers

TitleStatusHype
Adaptive Feature Discrimination and Denoising for Asymmetric Text Matching—0
DeepSpace: Dynamic Spatial and Source Cue Based Source Separation for Dialog Enhancement—0
Bayesian Cramér-Rao Bound Estimation with Score-Based Models—0
Almost Unsupervised Text to Speech and Automatic Speech Recognition—0
Accelerated graph-based spectral polynomial filters—0
EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems—0
Eformer: Edge Enhancement based Transformer for Medical Image Denoising—0
ELAICHI: Enhancing Low-resource TTS by Addressing Infrequent and Low-frequency Character Bigrams—0
Bayesian Conditional GAN for MRI Brain Image Synthesis—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
Batch-less stochastic gradient descent for compressive learning of deep regularization for image denoising—0
Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models—0
Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models—0
Efficient Two-Dimensional Sparse Coding Using Tensor-Linear Combination—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
Adapting Language Models for Non-Parallel Author-Stylized Rewriting—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising—0
Efficient Training for Visual Tracking with Deformable Transformer—0
Efficient Unsupervised NMT for Related Languages with Cross-Lingual Language Models and Fidelity Objectives—0
Basis Prediction Networks for Effective Burst Denoising with Large Kernels—0
Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees—0
Accelerated graph-based nonlinear denoising filters—0
Deep priors for satellite image restoration with accurate uncertainties—0
Deep Point Set Resampling via Gradient Fields—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