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 6826–6850 of 7282 papers

TitleStatusHype
Modeling documents with Generative Adversarial Networks—0
Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching—0
Joint denoising and distortion correction of atomic scale scanning transmission electron microscopy images—0
Correlation Preserving Sparse Coding Over Multi-level Dictionaries for Image Denoising—0
Local Sparse Approximation for Image Restoration with Adaptive Block Size Selection—0
Deeply Aggregated Alternating Minimization for Image Restoration—0
A Multilinear Tongue Model Derived from Speech Related MRI Data of the Human Vocal TractCode0
Cloud Dictionary: Sparse Coding and Modeling for Point CloudsCode0
Super-resolution Reconstruction of SAR Image based on Non-Local Means Denoising Combined with BP Neural Network—0
Sparse Factorization Layers for Neural Networks with Limited Supervision—0
Generalized Deep Image to Image RegressionCode0
An Empirical Study of ADMM for Nonconvex Problems—0
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence—0
Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis—0
Microseismic events enhancement and detection in sensor arrays using autocorrelation based filtering—0
Joint Visual Denoising and Classification using Deep LearningCode0
A Non-Local Means Approach for Gaussian Noise Removal from Images using a Modified Weighting Kernel—0
Split LBI: An Iterative Regularization Path with Structural Sparsity—0
A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter—0
Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.—0
Wasserstein Training of Restricted Boltzmann Machines—0
Unsupervised Learning from Noisy Networks with Applications to Hi-C Data—0
Select-and-Sample for Spike-and-Slab Sparse Coding—0
Proximal Deep Structured Models—0
Graph-Based Manifold Frequency Analysis for Denoising—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