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 5101–5125 of 7282 papers

TitleStatusHype
Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI—0
A novel ECG signal denoising filter selection algorithm based on conventional neural networks—0
DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs—0
Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network—0
Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling—0
Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation—0
FN-Net:Remove the Outliers by Filtering the Noise—0
Noise-specific denoising method with applications to high-frequency ultrasonic images—0
Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation—0
Online Deep Learning based on Auto-Encoder—0
Improving Neural Machine Translation by Denoising Training—0
Joint denoising and HDR for RAW video sequences—0
Machine Learning Enhances Algorithms for Quantifying Non-Equilibrium Dynamics in Correlation Spectroscopy Experiments to Reach Frame-Rate-Limited Time Resolution—0
Improving the quality control of seismic data through active learning—0
Convolutional Neural Networks for Spherical Signal Processing via Spherical Haar Tight Framelets—0
Predicting waves in fluids with deep neural network—0
Diffusion Models Using a Single Equation—0
Balancing the Style-Content Trade-Off in Sentiment Transfer UsingPolarity-Aware Denoising—0
ULF: Cross-Validation for Weak Supervision—0
On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent—0
Representation Learning for Resource-Constrained Keyphrase Generation—0
ALA: Naturalness-aware Adversarial Lightness Attack—0
S5 Framework: A Review of Self-Supervised Shared Semantic Space Optimization for Multimodal Zero-Shot Learning—0
DEEP: DEnoising Entity Pre-training for Neural Machine Translation—0
MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural NetworksCode0
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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