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

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Showing 6351–6375 of 7282 papers

TitleStatusHype
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
A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy ImagesCode1
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds DefenseCode0
Residual Dense Network for Image RestorationCode0
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
2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation—0
Adversarial Signal Denoising with Encoder-Decoder Networks—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
On Stacked Denoising Autoencoder based Pre-training of ANN for Isolated Handwritten Bengali Numerals Dataset Recognition—0
Denoising Weak Lensing Mass Maps with Deep Learning—0
Non-local Meets Global: An Integrated Paradigm for Hyperspectral DenoisingCode1
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
Non-Local Video Denoising by CNNCode0
Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal LearningCode0
Model-blind Video Denoising Via Frame-to-frame TrainingCode0
Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray DenoisingCode0
Iterative Residual CNNs for Burst Photography ApplicationsCode0
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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