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 5176–5200 of 7282 papers

TitleStatusHype
HINet: Half Instance Normalization Network for Image RestorationCode1
Spelling Correction with Denoising Transformer—0
Optimal Receive Beamforming for Over-the-Air Computation—0
EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models—0
Disentangling Noise from Images: A Flow-Based Image Denoising Neural NetworkCode1
Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds—0
A Bregman Learning Framework for Sparse Neural NetworksCode1
Graph Feature Gating Networks—0
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection—0
Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation—0
LINN: Lifting Inspired Invertible Neural Network for Image DenoisingCode0
Real-Time Video Super-Resolution by Joint Local Inference and Global Parameter Estimation—0
Towards Interpretable and Transferable Speech Emotion Recognition: Latent Representation Based Analysis of Features, Methods and Corpora—0
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series—0
Blind microscopy image denoising with a deep residual and multiscale encoder/decoder networkCode0
Simultaneous Denoising and Localization Network for Photoacoustic Target Localization—0
Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation StudyCode0
Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks—0
A Refined Inertial DC Algorithm for DC Programming—0
Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation—0
Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks—0
Weakly Supervised Volumetric Segmentation via Self-taught Shape Denoising ModelCode0
SE-DAE: Style-Enhanced Denoising Auto-Encoder for Unsupervised Text Style Transfer—0
AWCD: An Efficient Point Cloud Processing Approach via Wasserstein Curvature—0
A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiologyCode1
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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