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 3076–3100 of 7282 papers

TitleStatusHype
Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition—0
Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network—0
Denoising Induction Motor Sounds Using an Autoencoder—0
Blind Denoising Autoencoder—0
Blind Deconvolution of Graph Signals: Robustness to Graph Perturbations—0
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms—0
Denoising Improves Latent Space Geometry in Text Autoencoders—0
Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator—0
Denoising Imaging Polarimetry by an Adapted BM3D Method—0
Denoising Hyperspectral Image with Non-i.i.d. Noise Structure—0
Blind Channel Estimation for Massive MIMO: A Deep Learning Assisted Approach—0
Analysis and Extension of Noisy-target Training for Unsupervised Target Signal Enhancement—0
Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet—0
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100 speed-up—0
Accelerating Diffusion Sampling via Exploiting Local Transition Coherence—0
Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning—0
Blind Biological Sequence Denoising with Self-Supervised Set Learning—0
Denoising Hamiltonian Network for Physical Reasoning—0
Denoising guarantees for optimized sampling schemes in compressed sensing—0
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation—0
Analysing Diffusion Segmentation for Medical Images—0
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders—0
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders—0
Blind2Sound: Self-Supervised Image Denoising without Residual Noise—0
Analog Image Denoising with an Adaptive Memristive Crossbar Network—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