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 4676–4700 of 7282 papers

TitleStatusHype
Point Cloud Denoising via Momentum Ascent in Gradient FieldsCode1
The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional recurrent Network for Multi Channel Speech Enhancement and Speech Recognition—0
MANet: Improving Video Denoising with a Multi-Alignment NetworkCode1
Pseudo Numerical Methods for Diffusion Models on ManifoldsCode6
C2N: Practical Generative Noise Modeling for Real-World DenoisingCode1
Multi-Channel Speech Denoising for Machine Ears—0
A Two-Stage U-Net for High-Fidelity Denoising of Historical RecordingsCode1
Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs—0
Speech Denoising in the Waveform Domain with Self-AttentionCode2
Ab-initio Contrast Estimation and Denoising of Cryo-EM ImagesCode1
Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition—0
Exploring the Devil in Graph Spectral Domain for 3D Point Cloud AttacksCode1
Open-set Adversarial Defense with Clean-Adversarial Mutual LearningCode1
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network—0
Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling—0
Monotonically Convergent Regularization by Denoising—0
A GAN-based Denoising Method for Chinese Stele and Rubbing Calligraphic Image—0
Reducing Redundancy in the Bottleneck Representation of the Autoencoders—0
InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training—0
Adaptive Mixing of Auxiliary Losses in Supervised LearningCode0
Supervision by Denoising for Medical Image Segmentation—0
Riemannian Score-Based Generative ModellingCode1
Exploring Self-Attention Mechanisms for Speech Separation—0
Bregman Plug-and-Play Priors—0
Image-to-Image MLP-mixer for Image ReconstructionCode0
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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