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 5926–5950 of 7282 papers

TitleStatusHype
Image denoising via K-SVD with primal-dual active set algorithm—0
Sinogram super-resolution and denoising convolutional neural network (SRCN) for limited data photoacoustic tomography—0
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks—0
Masking schemes for universal marginalisers—0
Adaptive Direction-Guided Structure Tensor Total Variation—0
Autoencoders as Weight Initialization of Deep Classification Networks for Cancer versus Cancer Studies—0
Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks—0
A Differentiable Perceptual Audio Metric Learned from Just Noticeable DifferencesCode1
Self-Supervised Fast Adaptation for Denoising via Meta-Learning—0
Learning Generative Models using Denoising Density EstimatorsCode0
Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning—0
Hypergraph Spectral Analysis and Processing in 3D Point Cloud—0
Speech Enhancement based on Denoising Autoencoder with Multi-branched EncodersCode0
Implementation of the VBM3D Video Denoising Method and Some VariantsCode1
Image Speckle Noise Denoising by a Multi-Layer Fusion Enhancement Method based on Block Matching and 3D Filtering—0
InSAR Phase Denoising: A Review of Current Technologies and Future Directions—0
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising—0
A Machine Learning Imaging Core using Separable FIR-IIR Filters—0
First image then video: A two-stage network for spatiotemporal video denoisingCode0
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial DefenseCode1
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximationCode1
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video ProcessingCode0
Label-Noise Robust Domain Adaptation—0
A Total Variation Denoising Method Based on Median Filter and Phase Consistency—0
Stacked DeBERT: All Attention in Incomplete Data for Text ClassificationCode1
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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