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 6376–6400 of 7282 papers

TitleStatusHype
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point CloudsCode0
I Can See Clearly Now : Image Restoration via De-Raining—0
Optical Fringe Patterns Filtering Based on Multi-Stage Convolution Neural Network—0
Nasal Patches and Curves for Expression-robust 3D Face RecognitionCode0
Soft Autoencoder and Its Wavelet Adaptation Interpretation—0
CFA Bayer image sequence denoising and demosaicking chain—0
SMPLR: Deep SMPL reverse for 3D human pose and shape recovery—0
FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural NetworksCode0
Residual Dense Network for Image RestorationCode0
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds DefenseCode0
Coupled Analysis Dictionary Learning to inductively learn inversion: Application to real-time reconstruction of Biomedical signals—0
A Multiscale Image Denoising Algorithm Based On Dilated Residual Convolution Network—0
Adversarial Signal Denoising with Encoder-Decoder Networks—0
2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation—0
3D Point Cloud Denoising via Bipartite Graph Approximation and Reweighted Graph Laplacian—0
Representation Learning for Spatial Graphs—0
Classifier and Exemplar Synthesis for Zero-Shot LearningCode0
Denoising Weak Lensing Mass Maps with Deep Learning—0
On Stacked Denoising Autoencoder based Pre-training of ANN for Isolated Handwritten Bengali Numerals Dataset Recognition—0
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder ApproachCode0
QR code denoising using parallel Hopfield networks—0
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs—0
Model-blind Video Denoising Via Frame-to-frame TrainingCode0
Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal LearningCode0
Non-Local Video Denoising by CNNCode0
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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