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 7051–7100 of 7282 papers

TitleStatusHype
Collaborative Total Variation: A General Framework for Vectorial TV Models—0
A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks—0
Unknown sparsity in compressed sensing: Denoising and inference—0
Neural NILM: Deep Neural Networks Applied to Energy DisaggregationCode0
Multiscale Adaptive Representation of Signals: I. The Basic Framework—0
Wasserstein Training of Boltzmann Machines—0
Parsimonious Labeling—0
NCSU\_SAS\_SAM: Deep Encoding and Reconstruction for Normalization of Noisy Text—0
A note on patch-based low-rank minimization for fast image denoising—0
Conservativeness of untied auto-encoders—0
Flexible Multi-layer Sparse Approximations of Matrices and Applications—0
Deep Denoising Auto-encoder for Statistical Speech Synthesis—0
Evaluation of Denoising Techniques for EOG signals based on SNR Estimation—0
Convergence rates for pretraining and dropout: Guiding learning parameters using network structure—0
Multi-Objective Optimization for Self-Adjusting Weighted Gradient in Machine Learning Tasks—0
Image Denoising via Adaptive Soft-Thresholding Based on Non-Local Samples—0
UniHIST: A Unified Framework for Image Restoration With Marginal Histogram Constraints—0
Propagated Image Filtering—0
Robust Regression on Image Manifolds for Ordered Label Denoising—0
L0TV: A New Method for Image Restoration in the Presence of Impulse Noise—0
Bayesian Inference for Neighborhood Filters With Application in Denoising—0
Robust Image Filtering Using Joint Static and Dynamic Guidance—0
Generalized Tensor Total Variation Minimization for Visual Data Recovery—0
Estimating User Location in Social Media with Stacked Denoising Auto-encoders—0
A Simple Yet Effective Improvement to the Bilateral Filter for Image Denoising—0
A Review Paper: Noise Models in Digital Image Processing—0
Noise in Structured-Light Stereo Depth Cameras: Modeling and its Applications—0
Filter characteristics in image decomposition with singular spectrum analysis—0
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
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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