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 1676–1700 of 7282 papers

TitleStatusHype
Stacked DeBERT: All Attention in Incomplete Data for Text ClassificationCode1
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximationCode1
Scale-wise Convolution for Image RestorationCode1
Fully Trainable and Interpretable Non-Local Sparse Models for Image RestorationCode1
Scientific Image Restoration AnywhereCode1
Penalty Method for Inversion-Free Deep Bilevel OptimizationCode1
Online matrix factorization for Markovian data and applications to Network Dictionary LearningCode1
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionCode1
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score MatchingCode1
Overcoming Data Limitation in Medical Visual Question AnsweringCode1
Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View SynthesisCode1
Memory-Efficient Hierarchical Neural Architecture Search for Image DenoisingCode1
Unsupervised Sketch-to-Photo SynthesisCode1
Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image ReconstructionCode1
Variational Denoising Network: Toward Blind Noise Modeling and RemovalCode1
Backpropagation-Friendly EigendecompositionCode1
Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted ImageCode1
A Bayesian Model of Dose-Response for Cancer Drug StudiesCode1
Probabilistic Noise2Void: Unsupervised Content-Aware DenoisingCode1
GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise ModelingCode1
Learning Deformable Kernels for Image and Video DenoisingCode1
TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial NetworksCode1
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy imagesCode1
Noise2Self: Blind Denoising by Self-SupervisionCode1
A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy ImagesCode1
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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