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 5076–5100 of 7282 papers

TitleStatusHype
Adaptive 3D descattering with a dynamic synthesis networkCode0
Graph Signal Restoration Using Nested Deep Algorithm Unrolling—0
DAEMA: Denoising Autoencoder with Mask AttentionCode1
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement—0
Diffusion Priors In Variational Autoencoders—0
Uncertainty-Guided Progressive GANs for Medical Image TranslationCode1
R2RNet: Low-light Image Enhancement via Real-low to Real-normal NetworkCode1
Progressive Joint Low-light Enhancement and Noise Removal for Raw ImagesCode1
Robust Matrix Factorization with Grouping Effect—0
ChaLearn Looking at People: Inpainting and Denoising challenges—0
Fast Monte Carlo Rendering via Multi-Resolution SamplingCode1
Symmetric Wasserstein AutoencodersCode0
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks—0
Multi-modal and frequency-weighted tensor nuclear norm for hyperspectral image denoising—0
Deformed2Self: Self-Supervised Denoising for Dynamic Medical ImagingCode1
Effective Snapshot Compressive-Spectral Imaging via Deep Denoising and Total Variation PriorsCode1
Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise—0
Extreme Low-Light Environment-Driven Image Denoising Over Permanently Shadowed Lunar Regions With a Physical Noise Model—0
Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image DenoisingCode1
Polarimetric Normal Stereo—0
EventZoom: Learning To Denoise and Super Resolve Neuromorphic Events—0
Adaptive Consistency Prior Based Deep Network for Image DenoisingCode1
Deep Convolutional Dictionary Learning for Image DenoisingCode1
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-ResolutionCode1
Pseudo 3D Auto-Correlation Network for Real Image 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