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 7101–7125 of 7282 papers

TitleStatusHype
A review of mean-shift algorithms for clustering—0
The conjugated null space method of blind PSF estimation and deconvolution optimization—0
Boosting of Image Denoising Algorithms—0
Supervised Dictionary Learning and Sparse Representation-A Review—0
Towards Biologically Plausible Deep Learning—0
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source SeparationCode0
Convergence of gradient based pre-training in Denoising autoencoders—0
Image denoising based on improved data-driven sparse representation—0
Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation—0
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models—0
A new ADMM algorithm for the Euclidean median and its application to robust patch regression—0
_0 Sparsifying Transform Learning with Efficient Optimal Updates and Convergence Guarantees—0
Combined modeling of sparse and dense noise for improvement of Relevance Vector Machine—0
Weighted Schatten p-Norm Minimization for Image Denoising with Local and Nonlocal Regularization—0
A multistep segmentation algorithm for vessel extraction in medical imaging—0
Denoising autoencoder with modulated lateral connections learns invariant representations of natural imagesCode0
Generative Class-conditional Autoencoders—0
The local low-dimensionality of natural images—0
Gradual training of deep denoising auto encoders—0
Domain-Adversarial Neural NetworksCode0
First order algorithms in variational image processing—0
Towards Deep Neural Network Architectures Robust to Adversarial ExamplesCode0
An Approach for Reducing Outliers of Non Local Means Image Denoising Filter—0
Risk Estimation Without Using Stein's Lemma -- Application to Image Denoising—0
PEWA: Patch-based Exponentially Weighted Aggregation for image denoising—0
Show:102550
← PrevPage 285 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