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 6726–6750 of 7282 papers

TitleStatusHype
Kernel-predicting convolutional networks for denoising monte carlo renderings.—0
Wide Inference Network for Image Denoising via Learning Pixel-distribution PriorCode0
Discriminative Optimization: Theory and Applications to Computer Vision Problems—0
Impulsive noise removal from color images with morphological filtering—0
Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing—0
A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising—0
Benchmarking Denoising Algorithms with Real Photographs—0
The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising—0
Convolutional Dictionary Learning: Acceleration and ConvergenceCode0
Hyper-Laplacian Regularized Unidirectional Low-Rank Tensor Recovery for Multispectral Image Denoising—0
Removing Rain From Single Images via a Deep Detail Network—0
Simultaneous Visual Data Completion and Denoising Based on Tensor Rank and Total Variation Minimization and Its Primal-Dual Splitting Algorithm—0
Additive Component Analysis—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Efficient Manifold and Subspace Approximations with SphereletsCode0
Class-specific image denoising using importance sampling—0
Learning and Evaluating Musical Features with Deep Autoencoders—0
When Image Denoising Meets High-Level Vision Tasks: A Deep Learning ApproachCode0
Recurrent Inference Machines for Solving Inverse ProblemsCode1
A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging—0
Class-specific Poisson denoising by patch-based importance sampling—0
Gated Orthogonal Recurrent Units: On Learning to ForgetCode0
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising—0
Global hard thresholding algorithms for joint sparse image representation and denoising—0
Jeffrey's prior sampling of deep sigmoidal networks—0
Show:102550
← PrevPage 270 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