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 7076–7100 of 7282 papers

TitleStatusHype
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction—0
Adaptive diffusion constrained total variation scheme with application to `cartoon + texture + edge' image decomposition—0
Self-Expressive Decompositions for Matrix Approximation and Clustering—0
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction—0
Deconstructing Principal Component Analysis Using a Data Reconciliation Perspective—0
Image Denoising using Optimally Weighted Bilateral Filters: A Sure and Fast Approach—0
Lateral Connections in Denoising Autoencoders Support Supervised LearningCode0
Robust exponential binary pattern storage in Little-Hopfield networks—0
Compression Artifacts Reduction by a Deep Convolutional NetworkCode0
Image Segmentation and Restoration Using Parametric Contours With Free Endpoints—0
Self-Tuned Deep Super Resolution—0
Image Denoising Using Low Rank Minimization With Modified Noise Estimation—0
Learning Multiple Visual Tasks while Discovering their Structure—0
Gradual Training Method for Denoising Auto Encoders—0
Convex Denoising using Non-Convex Tight Frame Regularization—0
A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images—0
Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder—0
GSNs : Generative Stochastic Networks—0
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace NormCode0
Designing A Composite Dictionary Adaptively From Joint Examples—0
Modelling Computational Resources for Next Generation Sequencing Bioinformatics Analysis of 16S rRNA Samples—0
Learning the Structure for Structured Sparsity—0
Sparse Bayesian Dictionary Learning with a Gaussian Hierarchical Model—0
Denoising Autoencoders for fast Combinatorial Black Box OptimizationCode1
Convex Optimization for Parallel Energy Minimization—0
Show:102550
← PrevPage 284 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