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 6976–7000 of 7282 papers

TitleStatusHype
Blind Image Denoising via Dependent Dirichlet Process Tree—0
Stationary signal processing on graphs—0
Robust Non-linear Regression: A Greedy Approach Employing Kernels with Application to Image Denoising—0
Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective—0
Denoising and Completion of 3D Data via Multidimensional Dictionary Learning—0
Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation—0
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain—0
Proposition of a Theoretical Model for Missing Data Imputation using Deep Learning and Evolutionary Algorithms—0
Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction—0
MMSE Estimation for Poisson Noise Removal in Images—0
Query-Based Single Document Summarization Using an Ensemble Noisy Auto-Encoder—0
Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising—0
An Efficient Statistical Method for Image Noise Level Estimation—0
Learning Nonlinear Spectral Filters for Color Image Reconstruction—0
Low-Rank Tensor Approximation With Laplacian Scale Mixture Modeling for Multiframe Image Denoising—0
Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising—0
Fast and Effective L0 Gradient Minimization by Region FusionCode0
External Patch Prior Guided Internal Clustering for Image Denoising—0
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels—0
Accelerated graph-based nonlinear denoising filters—0
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) - The _0 Method—0
Principal Basis Analysis in Sparse Representation—0
Performance Limits of Stochastic Sub-Gradient Learning, Part I: Single Agent Case—0
Cascading Denoising Auto-Encoder as a Deep Directed Generative Model—0
Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and Denoising Autoencoders—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