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 6551–6575 of 7282 papers

TitleStatusHype
Denoising Distant Supervision for Relation Extraction via Instance-Level Adversarial Training—0
Unsupervised Learning with Stein's Unbiased Risk EstimatorCode0
A Nonlocal InSAR Filter for High-Resolution DEM Generation from TanDEM-X Interferograms—0
Distributed Cartesian Power Graph Segmentation for Graphon Estimation—0
Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products—0
Rate-Optimal Denoising with Deep Neural Networks—0
Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial IterationsCode0
State-Denoised Recurrent Neural Networks—0
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration—0
Multi-level Wavelet-CNN for Image RestorationCode0
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal DenoisingCode0
Network Enhancement: a general method to denoise weighted biological networks—0
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-ResolutionCode0
Moiré Photo Restoration Using Multiresolution Convolutional Neural NetworksCode0
Acceleration of RED via Vector ExtrapolationCode0
Perceptually Optimized Generative Adversarial Network for Single Image Dehazing—0
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising—0
Image Denoising via Collaborative Dual-Domain Patch Filtering—0
A Missing Information Loss function for implicit feedback datasets—0
Ladder Networks for Emotion Recognition: Using Unsupervised Auxiliary Tasks to Improve Predictions of Emotional Attributes—0
Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation—0
Deep Speech Denoising with Vector Space Projections—0
Generative Model for Heterogeneous Inference—0
Joint Enhancement and Denoising Method via Sequential DecompositionCode0
Unsupervised Natural Language Generation with Denoising AutoencodersCode0
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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