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 5476–5500 of 7282 papers

TitleStatusHype
Separating and denoising seismic signals with dual-path recurrent neural network architecture—0
Distribution Conditional Denoising: A Flexible Discriminative Image Denoiser—0
The Gaussian Process Latent Autoregressive Model—0
Spectral Domain Spline Graph Filter Bank—0
Legacy Photo Editing with Learned Noise PriorCode1
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition—0
Speech Denoising with Auditory ModelsCode1
Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric GuidanceCode1
Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches—0
Graph Signal Recovery Using Restricted Boltzmann MachinesCode0
Robust super-resolution depth imaging via a multi-feature fusion deep networkCode1
TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging ProblemsCode1
Liquid Warping GAN with Attention: A Unified Framework for Human Image SynthesisCode2
Plug-And-Play Learned Gaussian-mixture Approximate Message Passing—0
Digging Deeper into CRNN Model in Chinese Text Images Recognition—0
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation—0
Denoising Score-Matching for Uncertainty Quantification in Inverse ProblemsCode0
Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder—0
Assessing Wireless Sensing Potential with Large Intelligent Surfaces—0
Automatic artifact removal of resting-state fMRI with Deep Neural Networks—0
Improving Speech Enhancement Performance by Leveraging Contextual Broad Phonetic Class Information—0
Channel Estimation for Large Intelligent Surface Aided MISO Communications: From LMMSE to Deep Learning Solutions—0
DANAE: a denoising autoencoder for underwater attitude estimationCode0
Learning to Drop: Robust Graph Neural Network via Topological DenoisingCode1
Noise Conscious Training of Non Local Neural Network powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT DenoisingCode1
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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