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 2051–2075 of 7282 papers

TitleStatusHype
Deep Learning strategies for ProtoDUNE raw data denoisingCode0
Learning Dynamics of Linear Denoising AutoencodersCode0
Deep Learning of Radiative Atmospheric Transfer with an AutoencoderCode0
Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal ProcessingCode0
Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Learning Generalizable 3D Manipulation With 10 DemonstrationsCode0
Learning-Based Reconstruction of FRI SignalsCode0
Learning Better Masking for Better Language Model Pre-trainingCode0
Learned Convolutional Sparse CodingCode0
Learned D-AMP: Principled Neural Network based Compressive Image RecoveryCode0
Deep learning cardiac motion analysis for human survival predictionCode0
Learning Deep CNN Denoiser Prior for Image RestorationCode0
Learning Generative Models using Denoising Density EstimatorsCode0
Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence ForecastingCode0
Unsupervised dynamic modeling of medical image transformationCode0
Deep Learning based Switching Filter for Impulsive Noise Removal in Color ImagesCode0
Educating Text Autoencoders: Latent Representation Guidance via DenoisingCode0
Large Graph Signal Denoising with Application to Differential PrivacyCode0
Deep learning-based deconvolution for interferometric radio transient reconstructionCode0
Deep Learning-Based Channel EstimationCode0
Automatic Tuning of Denoising Algorithms Parameters Without Ground TruthCode0
Language Embeddings for Typology and Cross-lingual Transfer LearningCode0
Language-Guided Diffusion Model for Visual GroundingCode0
Language Model Preference Evaluation with Multiple Weak EvaluatorsCode0
Language-Aware Multilingual Machine Translation with Self-Supervised LearningCode0
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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