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 3301–3325 of 7282 papers

TitleStatusHype
Bayesian Conditional GAN for MRI Brain Image Synthesis—0
Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models—0
Batch-less stochastic gradient descent for compressive learning of deep regularization for image denoising—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising—0
Basis Prediction Networks for Effective Burst Denoising with Large Kernels—0
Deep priors for satellite image restoration with accurate uncertainties—0
Deep Point Set Resampling via Gradient Fields—0
Deep Point Cloud Reconstruction—0
Deep Plug-and-play Prior for Low-rank Tensor Completion—0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising—0
Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees—0
Deep Photon Mapping—0
All-optical image denoising using a diffractive visual processor—0
DeepPCR: Parallelizing Sequential Operations in Neural Networks—0
Balancing the Style-Content Trade-Off in Sentiment Transfer UsingPolarity-Aware Denoising—0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
Deep Nonparametric Estimation of Intrinsic Data Structures by Chart Autoencoders: Generalization Error and Robustness—0
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense—0
Balancing Act: Distribution-Guided Debiasing in Diffusion Models—0
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data—0
Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Show:102550
← PrevPage 133 of 292Next →
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