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 5926–5950 of 7282 papers

TitleStatusHype
Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information—0
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network—0
Restore from Restored: Video Restoration with Pseudo Clean Video—0
Restore from Restored: Single Image Denoising with Pseudo Clean Image—0
Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks—0
Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy ChannelCode0
Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family ObservablesCode0
ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising—0
Gradient Statistics Aware Power Control for Over-the-Air Federated Learning—0
Gauss-Newton Unrolled Neural Networks and Data-driven Priors for Regularized PSSE with Robustness—0
When deep denoising meets iterative phase retrieval—0
XGPT: Cross-modal Generative Pre-Training for Image Captioning—0
Flashlight CNN Image DenoisingCode0
Detecting Patch Adversarial Attacks with Image ResidualsCode0
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training—0
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning—0
Momentum-Net for Low-Dose CT Image Reconstruction—0
Multi-Cycle-Consistent Adversarial Networks for CT Image Denoising—0
Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance NormalizationCode0
Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling—0
An Optimal Statistical and Computational Framework for Generalized Tensor Estimation—0
Blind Channel Estimation for Massive MIMO: A Deep Learning Assisted Approach—0
The Maximum Entropy on the Mean Method for Image Deblurring—0
Generalized Octave Convolutions for Learned Multi-Frequency Image CompressionCode0
Unsupervised Denoising for Satellite Imagery using Wavelet Subband CycleGAN—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