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 5626–5650 of 7282 papers

TitleStatusHype
Knowledge Capture and Replay for Continual Learning—0
D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised ActivationsCode0
Denoising-based Turbo Message Passing for Compressed Video Background Subtraction—0
An Empirical Review of Adversarial DefensesCode0
Online Joint Topology Identification and Signal Estimation from Streams with Missing Data—0
Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments—0
Convex Regularization Behind Neural Reconstruction—0
Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning—0
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading ComprehensionCode0
Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering—0
Color Image Restoration Exploiting Inter-channel Correlation with a 3-stage CNNCode0
Noise2Kernel: Adaptive Self-Supervised Blind Denoising using a Dilated Convolutional Kernel Architecture—0
Towards speech enhancement using a variational U-Net architecture—0
H-FND: Hierarchical False-Negative Denoising for Distant Supervision Relation ExtractionCode0
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals—0
Event Guided Denoising for Multilingual Relation Learning—0
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation—0
SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising—0
Multi-mode Core Tensor Factorization based Low-Rankness and Its Applications to Tensor Completion—0
EVRNet: Efficient Video Restoration on Edge Devices—0
Lookahead optimizer improves the performance of Convolutional Autoencoders for reconstruction of natural images—0
Train Hard, Finetune Easy: Multilingual Denoising for RDF-to-Text Generation—0
Event-Guided Denoising for Multilingual Relation Learning—0
Meta Ensemble for Japanese-Chinese Neural Machine Translation: Kyoto-U+ECNU Participation to WAT 2020—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