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 6576–6600 of 7282 papers

TitleStatusHype
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model—0
Example-based super-resolution for point-cloud video—0
Semantic Adversarial ExamplesCode0
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising NetworksCode0
Noise2Noise: Learning Image Restoration without Clean DataCode1
Correction by Projection: Denoising Images with Generative Adversarial Networks—0
Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping—0
Cross-domain Recommendation via Deep Domain Adaptation—0
Nonlocality-Reinforced Convolutional Neural Networks for Image DenoisingCode0
Learning Filter Bank Sparsifying Transforms—0
Enhancement of land-use change modeling using convolutional neural networks and convolutional denoising autoencoders—0
Poisson Image Denoising Using Best Linear Prediction: A Post-processing Framework—0
prDeep: Robust Phase Retrieval with a Flexible Deep NetworkCode0
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification—0
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse CodingCode0
Structured Uncertainty Prediction NetworksCode0
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery—0
Divide, Denoise, and Defend against Adversarial Attacks—0
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative RefinementCode0
Fast, Trainable, Multiscale Denoising—0
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network—0
Joint Demosaicing and Denoising with Perceptual Optimization on a Generative Adversarial Network—0
Recovering Loss to Followup Information Using Denoising Autoencoders—0
Temporal and volumetric denoising via quantile sparse image prior—0
client2vec: Towards Systematic Baselines for Banking Applications—0
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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