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 5826–5850 of 7282 papers

TitleStatusHype
Quantum mechanics-based signal and image representation: application to denoising—0
Image Denoising Using Sparsifying Transform Learning and Weighted Singular Values Minimization—0
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New StrategyCode1
Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic ScenesCode1
Flows for simultaneous manifold learning and density estimationCode1
When to Use Convolutional Neural Networks for Inverse Problems—0
Plug-and-Play Algorithms for Large-scale Snapshot Compressive ImagingCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Laplacian Denoising Autoencoder—0
A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems—0
A Physics-based Noise Formation Model for Extreme Low-light Raw DenoisingCode1
GAN-based Priors for Quantifying UncertaintyCode1
A Review of Multi-Objective Deep Learning Speech Denoising Methods—0
Patch-based Non-Local Bayesian Networks for Blind Confocal Microscopy Denoising—0
Multiscale Sparsifying Transform Learning for Image DenoisingCode0
Robust and On-the-fly Dataset Denoising for Image Classification—0
Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques—0
Hyperspectral Mixed Noise Removal By L1-Norm-Based Subspace RepresentationCode0
Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic CamerasCode1
Solving Inverse Problems with a Flow-based Noise Model—0
Burst Denoising of Dark ImagesCode1
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance—0
Energy-Based Processes for Exchangeable Data—0
CycleISP: Real Image Restoration via Improved Data SynthesisCode1
Gated Texture CNN for Efficient and Configurable Image DenoisingCode0
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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