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 5851–5875 of 7282 papers

TitleStatusHype
A CNN-Based Blind Denoising Method for Endoscopic ImagesCode1
Stochastic Frequency Masking to Improve Super-Resolution and Denoising NetworksCode1
Learning Enriched Features for Real Image Restoration and EnhancementCode1
Non-Local Part-Aware Point Cloud Denoising—0
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification—0
Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information—0
W2S: Microscopy Data with Joint Denoising and Super-Resolution for Widefield to SIM MappingCode1
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based PenaltiesCode1
3D Quasi-Recurrent Neural Network for Hyperspectral Image DenoisingCode1
Restore from Restored: Single Image Denoising with Pseudo Clean Image—0
Restore from Restored: Video Restoration with Pseudo Clean Video—0
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network—0
Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks—0
Show, Edit and Tell: A Framework for Editing Image CaptionsCode1
Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy ChannelCode0
ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising—0
Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family ObservablesCode0
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
Unsupervised Interpretable Representation Learning for Singing Voice SeparationCode1
XGPT: Cross-modal Generative Pre-Training for Image Captioning—0
When deep denoising meets iterative phase retrieval—0
Flashlight CNN Image DenoisingCode0
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training—0
Detecting Patch Adversarial Attacks with Image ResidualsCode0
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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