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

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Showing 7151–7175 of 7282 papers

TitleStatusHype
Fast Separable Non-Local Means—0
The jump set under geometric regularisation. Part 2: Higher-order approaches—0
The jump set under geometric regularisation. Part 1: Basic technique and first-order denoising—0
Imaging with Kantorovich-Rubinstein discrepancy—0
Adaptive Image Denoising by Targeted Databases—0
On a new formulation of nonlocal image filters involving the relative rearrangement—0
Active Learning and Best-Response Dynamics—0
A hybrid neuro--wavelet predictor for QoS control and stability—0
Scheduled denoising autoencodersCode0
Denosing Using Wavelets and Projections onto the L1-Ball—0
Image Tag Completion by Low-rank Factorization with Dual Reconstruction Structure Preserved—0
Variational inference of latent state sequences using Recurrent Networks—0
Analyzing noise in autoencoders and deep networks—0
A Comprehensive Approach to Mode Clustering—0
Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout—0
Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising—0
Covariance Trees for 2D and 3D Processing—0
A Primal-Dual Algorithm for Higher-Order Multilabel Markov Random Fields—0
Multipoint Filtering with Local Polynomial Approximation and Range Guidance—0
CID: Combined Image Denoising in Spatial and Frequency Domains Using Web Images—0
Filter Forests for Learning Data-Dependent Convolutional Kernels—0
Super-Resolving Noisy Images—0
Video Motion Segmentation Using New Adaptive Manifold Denoising Model—0
Weighted Nuclear Norm Minimization with Application to Image Denoising—0
Combined Approach for Image Segmentation—0
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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