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 5801–5825 of 7282 papers

TitleStatusHype
The Role of Redundant Bases and Shrinkage Functions in Image Denoising—0
AMP-Net: Denoising based Deep Unfolding for Compressive Image SensingCode1
Unsupervised Opinion Summarization with Noising and DenoisingCode1
Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning—0
Frequency-Weighted Robust Tensor Principal Component Analysis—0
CommUnet: U-net decoder for convolutional codes in communication—0
Superkernel Neural Architecture Search for Image Denoising—0
Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging—0
Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm—0
Distributed Evolution of Deep Autoencoders—0
Self-Supervised training for blind multi-frame video denoising—0
Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems—0
Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data—0
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned GenerationCode1
Learning from Rules Generalizing Labeled ExemplarsCode1
Test-Time Adaptable Neural Networks for Robust Medical Image SegmentationCode1
Deep Manifold Prior—0
Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders—0
Transfer learning and subword sampling for asymmetric-resource one-to-many neural translationCode0
Plug-and-play ISTA converges with kernel denoisersCode0
Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation—0
WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech EnhancementCode1
Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems—0
Simultaneous Denoising and Dereverberation Using Deep Embedding Features—0
Anomaly Detection and Prototype Selection Using Polyhedron CurvatureCode0
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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