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 5901–5925 of 7282 papers

TitleStatusHype
Deep Manifold Prior—0
Transfer learning and subword sampling for asymmetric-resource one-to-many neural translationCode0
Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders—0
Plug-and-play ISTA converges with kernel denoisersCode0
Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation—0
Simultaneous Denoising and Dereverberation Using Deep Embedding Features—0
Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems—0
Anomaly Detection and Prototype Selection Using Polyhedron CurvatureCode0
Quantum mechanics-based signal and image representation: application to denoising—0
Image Denoising Using Sparsifying Transform Learning and Weighted Singular Values Minimization—0
Laplacian Denoising Autoencoder—0
When to Use Convolutional Neural Networks for Inverse Problems—0
A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems—0
A Review of Multi-Objective Deep Learning Speech Denoising Methods—0
Multiscale Sparsifying Transform Learning for Image DenoisingCode0
Patch-based Non-Local Bayesian Networks for Blind Confocal Microscopy Denoising—0
Robust and On-the-fly Dataset Denoising for Image Classification—0
Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques—0
Solving Inverse Problems with a Flow-based Noise Model—0
Hyperspectral Mixed Noise Removal By L1-Norm-Based Subspace RepresentationCode0
Energy-Based Processes for Exchangeable Data—0
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance—0
Gated Texture CNN for Efficient and Configurable Image DenoisingCode0
Non-Local Part-Aware Point Cloud Denoising—0
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification—0
Show:102550
← PrevPage 237 of 292Next →
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