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 6476–6500 of 7282 papers

TitleStatusHype
Hierarchical Relational Networks for Group Activity Recognition and RetrievalCode0
Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection—0
Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective—0
Optimum window length of Savitzky-Golay filters with arbitrary order—0
Autoencoders, Kernels, and Multilayer Perceptrons for Electron Micrograph Restoration and CompressionCode0
Automatic Foreground Extraction from Imperfect Backgrounds using Multi-Agent Consensus Equilibrium—0
Class-Aware Fully-Convolutional Gaussian and Poisson DenoisingCode0
PACO: Global Signal Restoration via PAtch COnsensus—0
Robust Compressive Phase Retrieval via Deep Generative PriorsCode0
A Pipeline for Lenslet Light Field Quality Enhancement—0
Deep Retinex Decomposition for Low-Light EnhancementCode0
Low Rank Regularization: A Review—0
Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial NetworkCode0
X-GANs: Image Reconstruction Made Easy for Extreme Cases—0
Application of Bounded Total Variation Denoising in Urban Traffic Analysis—0
The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling—0
Investigating accuracy of pitch-accent annotations in neural network-based speech synthesis and denoising effects—0
Cooperative Denoising for Distantly Supervised Relation Extraction—0
Deep Graph Laplacian Regularization for Robust Denoising of Real ImagesCode0
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured DataCode0
Deep End-to-end Fingerprint Denoising and InpaintingCode0
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks—0
U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and InpaintingCode0
Face De-Spoofing: Anti-Spoofing via Noise ModelingCode0
User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction—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